**here**for the latest version.

# Algorithms

The Splunk Machine Learning Toolkit supports the algorithms listed here. In addition to the examples included in the Splunk Machine Learning Toolkit, you can find more examples of these algorithms on the scikit-learn website.

The following algorithms use the `fit`

and `apply`

commands within the Splunk Machine Learning Toolkit. For information on the steps taken by these commands, please review the Understanding the fit and apply commands document.

Looking for information on using the `score`

command? Please navigate to the score command documentation for details.

### ML-SPL Quick Reference Guide

Download the Machine Learning Toolkit Quick Reference Guide for a handy cheat sheet of ML-SPL commands and machine learning algorithms used in the Splunk Machine Learning Toolkit. This document is also available in Japanese.

### ML-SPL Performance App

Download the ML-SPL Performance App for the Machine Learning Toolkit to use performance results for guidance and benchmarking purposes in your own environment.

### Extend the algorithms you can use for your models

The algorithms listed here and in the ML-SPL Quick Reference Guide are available natively in the Splunk Machine Learning Toolkit. You can also base your algorithm on over 300 open source Python algorithms from scikit-learn, pandas, statsmodel, numpy and scipy libraries available through the Python for Scientific Computing add-on in Splunkbase.

For information on how to import an algorithm from the Python for Scientific Computing add-on into the Splunk Machine Learning Toolkit, see the ML-SPL API Guide.

### Add algorithms through GitHub

On-prem customers looking for solutions that fall outside of the 30 native algorithms can use GitHub to add more algorithms. Solve custom uses cases through sharing and reusing algorithms in the Splunk Community for MLTK on GitHub. Here you can also learn about new machine learning algorithms from the Splunk open source community, and help fellow users of the toolkit.

Cloud customers can also use GitHub to add more algorithms via an app. The Splunk GitHub for Machine learning app provides access to custom algorithms and is based on the Machine Learning Toolkit open source repo. Cloud customers need to create a support ticket to have this app installed.

## Anomaly Detection

Anomaly detection algorithms detect anomalies and outliers in numerical or categorical fields.

### DensityFunction

The DensityFunction algorithm provides a consistent and streamlined workflow to create and store density functions and utilize them for anomaly detection. DensityFunction allows for grouping of the data using a `by`

clause, where for each group a separate density function is fitted and stored.

The DensityFunction algorithm supports the following three continuous probability density functions: Normal, Exponential, and Gaussian Kernel Density Estimation (Gaussian KDE).

Using the DensityFunction algorithm requires running version 1.4 of the Python for Scientific Computing add-on.

The accuracy of the anomaly detection for DensityFunction depends on the quality and the size of the training dataset, how accurately the fitted distribution models the underlying process that generates the data, and the value chosen for the `threshold`

parameter.

Follow these guidelines to make your models perform more accurately:

- Aim for fitted distributions to have a cardinality (training dataset size) of at least 50. If you cannot collect more training data, create fewer groups of data using the
`by`

clause, giving you more datapoints per group. - The
`threshold`

parameter has a default value, but ideally the value for`threshold`

,`lower_threshold`

, or`upper_threshold`

are chosen based on experimentation as guided by domain knowledge. - Continue tuning the
`threshold`

parameter until you are satisfied with the results. - Always inspect the model using the
`summary`

command. - If the distribution of the data changes through time, re-train your models frequently.

**Parameters**

- Valid values for the
`dist`

parameter include: norm (normal distribution), expon (exponential distribution), gaussian_kde (Gaussian KDE distribution), and auto (automatic selection). - The
`dist`

parameter default is auto. When set to auto, norm (normal distribution), expon (exponential distribution), and gaussian_kde (Gaussian KDE distribution) all run, with the best results returned. - The
`metric`

parameter calculates the distance between the sampled dataset from the density function and the training dataset. - Valid metrics for the
`metric`

parameter include: kolmogorov_smirnov and wasserstein. - The
`metric`

parameter default is wasserstein. - The
`cardinality`

value generated by the`summary`

command represents the number of data points used when fitting the selected density function. - The
`distance`

value generated by the`summary`

command represents the metric type used when calculating the distance as well as the distance between the sampled data points from the density function and the training dataset. - The
`mean`

value is the mean of the density function. - The value for
`std`

represents the standard deviation of the density function. - A value under
`other`

represents any parameters other than`mean`

and`std`

as applicable. In the case of Gaussian KDE,`other`

could show parameter size or bandwidth. - The
`type`

field shows both the chosen density function as well as if the`dist`

parameter is set to auto. - The
`show_density`

parameter default is False. If the parameter is set to True, the density of each data point will be provided as output in a new field called`ProbabilityDensity`

. - The output for
`ProbabilityDensity`

is the probability density of the data point according to the fitted probability density. This output is provided when the`show_density`

parameter is set to True. - The
`fit`

command will fit a probability density function over the data, optionally store the resulting distribution's parameters in a model file, and output the outlier in a new field called`IsOutlier`

. - The output for
`IsOutlier`

is a list of labels. Number 1 represents outliers, and 0 represents inliers, assigned to each data point. Outliers are detected based on the values set for the`threshold`

parameter. Inspect the`IsOutlier`

results column to see how well the outlier detection is performing. - The parameters
`threshold`

,`lower_threshold`

, and`upper_threshold`

control the outlier detection process. - The
`threshold`

parameter is the center of the outlier detection process. It represents the percentage of the area under the density function and has a value between 0.000000001 (refers to ~0%) and 1 (refers to 100%). The`threshold`

parameter guides the DensityFunction algorithm to mark outlier areas on the fitted distribution. For example, if`threshold=0.01`

, then 1% of the fitted density function will be set as the outlier area. - The
`threshold`

parameter default value is 0.01. - The output for
`BoundaryRanges`

is the boundary ranges of outliers on the density function which are set according to the values of the`threshold`

parameter. - Each boundary region has three values: boundary opening point, boundary closing point, and percentage of boundary region.
- The syntax follows the convention of
`[[first_boundary_region], [second_boundary_region],… ,[n_th boundary_region]]`

. - In cases of a single boundary region, the value for the percentage of boundary region is equal to the
`threshold`

parameter value.

- The syntax follows the convention of
`BoundaryRanges`

is calculated as an approximation and will be empty in the following two cases:- Where the density function has a sharp peak from low standard deviation.
- When there are a low number of data points.

- Data points that are exactly at the boundary opening or closing point are assigned as inliers. An opening or closing point is determined by the density function in use.
- Normal density function has left and right boundary regions. Data points on the left of the left boundary closing point, and data points on the right of the right boundary opening point are assigned as outliers.
- Exponential density function has one boundary region. Data points on the right of the right boundary opening point are assigned as outliers.
- Gaussian KDE density function can have one or more boundary regions, depending on the number of peaks and dips within the density function. Data points in these boundary regions are assigned as outliers. In cases of boundary regions to the left or right, guidelines from Normal density function apply. As the shape for Gaussian KDE density function can differ from dataset to dataset, you do not consistently observe left and right boundary regions.

**Syntax**

| fit DensityFunction <field> [by "<field1>[,<field2>,....<field5>]"] [into <model name>] [dist=<str>] [show_density=true|false] [threshold=<float>|lower_threshold=<float>|upper_threshold=<float>] [metric=<str>]

You can apply the saved model to new data with the `apply`

command, with the option to update the parameters for `threshold`

, `lower_threshold`

, `upper_threshold`

, and `show_density`

. Parameters for `dist`

and `metric`

cannot be applied at this stage, and any new values provided will be ignored.

| apply <model name> [threshold=<float>|lower_threshold=<float>|upper_threshold=<float>] [show_density=true|false]

You can inspect the model learned by DensityFunction with the `summary`

command.

| summary <model name>

**Syntax constraints**

- Fields within the
`by`

clause must be given in quotation marks. - The maximum number of fields within the
`by`

clause is 5. - The total number of groups calculated with the
`by`

clause can not exceed 1024. In an example clause of`by "DayOfWeek,HourOfDay"`

there are two fields: one for`DayOfWeek`

and one for`HourOfDay`

. As there are seven days in a week, there are seven groups for`DayOfWeek`

. As there are twenty-four hours in a day, there are twenty-four groups for`HourOfDay`

. Meaning the total number of groups calculated with the by clause is`7*24= 168`

.- The limited number of groups prevents model files from growing too large. You can increase the limit by changing the value of
`max_groups`

in the DensityFunction settings. Larger limits mean larger model files and longer load times when running`apply`

. - Decrease
`max_kde_parameter_size`

to allow for the increase of`max_groups`

. This change keeps model sizes small while allowing for increased groups.

- The limited number of groups prevents model files from growing too large. You can increase the limit by changing the value of
- The parameters
`threshold`

,`lower_threshold`

, and`upper_threshold`

must be within the range of 0.00000001 to 1. - If the parameters of
`lower_threshold`

and`upper_threshold`

are both provided, the summation of these parameters must be less than 1 (100%). - The
`threshold`

and`lower_threshold`

/`upper_threshold`

parameters can not be specified together. - Exponential density function and Gaussian KDE density function only support the
`threshold`

. - Exponential density function and Gaussian KDE density function do not support
`lower_threshold`

or`upper_threshold`

. - Normal density function supports either
`threshold`

or`lower_threshold`

/`upper_threshold`

. - The parameters
`lower_threshold`

and`upper_threshold`

can be used with only Normal density function.

**Examples**

The following example shows DensityFunction on a dataset with the `fit`

command.

| inputlookup call_center.csv | eval _time=strptime(_time, "%Y-%m-%dT%H:%M:%S") | bin _time span=15m | eval HourOfDay=strftime(_time, "%H") | eval BucketMinuteOfHour=strftime(_time, "%M") | eval DayOfWeek=strftime(_time, "%A") | stats max(count) as Actual by HourOfDay,BucketMinuteOfHour,DayOfWeek,source,_time | fit DensityFunction Actual by "HourOfDay,BucketMinuteOfHour,DayOfWeek" into mymodel

The following example shows DensityFunction on a dataset with the `apply`

command.

| inputlookup call_center.csv | eval _time=strptime(_time, "%Y-%m-%dT%H:%M:%S") | bin _time span=15m | eval HourOfDay=strftime(_time, "%H") | eval BucketMinuteOfHour=strftime(_time, "%M") | eval DayOfWeek=strftime(_time, "%A") | stats max(count) as Actual by HourOfDay,BucketMinuteOfHour,DayOfWeek,source,_time | apply mymodel threshold=0.02

The following example shows DensityFunction on a dataset with the `summary`

command.

| summary mymodel

The following example shows `BoundaryRages`

on a test set. In this example the threshold is set to 30% (0.3). The first row has a left boundary range which starts at -Infinity and goes up to the number 44.6912. The area of the left boundary range is 15% of the total area under the density function. It has also a right boundary range which starts at a number 518.3088 and goes up to Infinity. Again, the area of the right boundary range is the same as the left boundary range with 15% of the total area under the density function. The areas of right and left boundary ranges add up to the threshold value of 30%. The third row has only one boundary range which starts at number 300.0943 and goes up to Infinity. The area of the boundary range is 30% of the area under the density function.

| inputlookup call_center.csv | eval _time=strptime(_time, "%Y-%m-%dT%H:%M:%S") | bin _time span=15m | eval HourOfDay=strftime(_time, "%H") | eval BucketMinuteOfHour=strftime(_time, "%M") | eval DayOfWeek=strftime(_time, "%A") | stats max(count) as Actual by HourOfDay, BucketMinuteOfHour, DayOfWeek, source, _time | fit DensityFunction Actual by "HourOfDay, BucketMinuteOfHour, DayOfWeek" threshold=0.3 into mymodel

### LocalOutlierFactor

The LocalOutlierFactor algorithm uses the scikit-learn Local Outlier Factor (LOF) to measure the local deviation of density of a given sample with respect to its neighbors. LocalOutlierFactor is an unsupervised outlier detection method. The anomaly score depends on how isolated the object is with respect to its neighbors.

For descriptions of the `n_neighbors`

, `leaf_size`

and other parameters, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html

Using the LocalOutlierFactor algorithm requires running version 1.3 or above of the Python for Scientific Computing add-on.

**Parameters**

- The
`anomaly_score`

parameter default is True. Disable this default by adding the`False`

keyword to the command. - The
`n_neighbors`

parameter default is 20 - The
`leaf_size`

parameter default is 30 - The
`p`

parameter is limited to`p >=1`

- The
`contamination`

parameter must be within the range of 0.0 (not included) to 0.5 (included) - The
`contamination`

parameter default is 0.1 - Options for the
`algorithm`

parameter include: brute, kd_tree, ball_tree, and auto. The default is auto. - The brute, kd_tree, ball_tree, and auto
`algorithm`

options have respective valid metrics. The default`metric`

for each is algorithm is minkowski.- Valid metrics for brute include: cityblock, euclidean, l1, l2, manhattan, chebyshev, minkowski, braycurtis, canberra, dice, hamming, jaccard, kulsinski, matching, rogerstanimoto, russellrao, sokalmichener, sokalsneath, cosine, correlation, sqeuclidean, and yule.
- Valid metrics for kd_tree include: cityblock, euclidean, l1, l2, manhattan, chebyshev, and minkowski.
- Valid metrics for ball_tree include: cityblock, euclidean, l1, l2, manhattan, chebyshev, minkowski, braycurtis, canberra, dice, hamming, jaccard, kulsinski, matching, rogerstanimoto, russellrao, sokalmichener, and sokalsneath.

- The output for LocalOutlierFactor is a list of labels titled
`is_outlier`

, assigned`1`

for outliers, and`-1`

for inliers

**Syntax**

fit LocalOutlierFactor <fields> [n_neighbors=<int>] [leaf_size=<int>] [p=<int>] [contamination=<float>] [metric=<str>] [algorithm=<str>] [anomaly_score=<true|false>]

**Syntax constraints**

- You cannot save LocalOutlierFactor models using the
`into`

keyword. This algorithm does not support saving models. - LocalOutlierFactor does not include the
`predict`

method.

**Example**

The following example uses LocalOutlierFactor on a test set.

| inputlookup iris.csv | fit LocalOutlierFactor petal_length petal_width n_neighbors=10 algorithm=kd_tree metric=minkowski p=1 contamination=0.14 leaf_size=10

### OneClassSVM

The OneClassSVM algorithm uses the scikit-learn OneClassSVM to fit a model from a set of features or fields for detecting anomalies and outliers, where features are expected to contain numerical values. OneClassSVM is an unsupervised outlier detection method.

For further information, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/svm.html#kernel-functions

**Parameters**

- The
`kernel`

parameter specifies the kernel type for using in the algorithm, where the default value is kernel is`rbf`

.- Kernel types include: linear, rbf, poly, and sigmoid.

- You can specify the upper bound on the fraction of training error as well as the lower bound of the fraction of support vectors using the
`nu`

parameter, where the default value is 0.5. - The
`degree`

parameter is ignored by all kernels except the polynomial kernel, where the default value is 3. `gamma`

is the kernel co-efficient that specifies how much influence a single data instance has, where the default value is`1/ number of features`

.- The independent term of
`coef0`

in the kernel function is only significant if you have polynomial or sigmoid function. - The term
`tol`

is the tolerance for stopping criteria. - The
`shrinking`

parameter determines whether to use the shrinking heuristic.

**Syntax**

fit OneClassSVM <fields> [into <model name>] [kernel=<str>] [nu=<float>] [coef0=<float>] [gamma=<float>] [tol=<float>] [degree=<int>] [shrinking=<true|false>]

- You can save OneClassSVM models using the
`into`

keyword. - You can apply the saved model later to new data with the
`apply`

command.

**Syntax constraints**

- After running the
`fit`

or`apply`

command, a new field named`isNormal`

is generated. This field defines whether a particular record (row) is normal (`isNormal=1`

) or anomalous (`isNormal=-1`

). - You cannot inspect the model learned by OneClassSVM with the
`summary`

command.

**Example**

The following example uses OneClassSVM on a test set.

... | fit OneClassSVM * kernel="poly" nu=0.5 coef0=0.5 gamma=0.5 tol=1 degree=3 shrinking=f into TESTMODEL_OneClassSVM

## Classifiers

Classifier algorithms predict the value of a categorical field.

The `kfold`

cross-validation command can be used with all Classifier algorithms. Learn more here.

### BernoulliNB

The BernoulliNB algorithm uses the scikit-learn BernoulliNB estimator to fit a model to predict the value of categorical fields where explanatory variables are assumed to be binary-valued. BernoulliNB is an implementation of the Naive Bayes classification algorithm. This algorithm supports incremental fit.

**Parameters**

- The
`alpha`

parameter controls Laplace/ Lidstone smoothing. The default value is 1.0. - The
`binarize`

parameter is a threshold that can be used for converting numeric field values to the binary values expected by BernoulliNB. The default value is 0.- If
`binarize=0`

is specified, the default, values > 0 are assumed to be 1, and values <= 0 are assumed to be 0.

- If
- The
`fit_prior`

Boolean parameter specifies whether to learn class prior probabilities. The default value is True. If`fit_prior=f`

is specified, classes are assumed to have uniform popularity.

**Syntax**

fit BernoulliNB <field_to_predict> from <explanatory_fields> [into <model name>] [alpha=<float>] [binarize=<float>] [fit_prior=<true|false>] [partial_fit=<true|false>]

You can save BernoulliNB models using the `into`

keyword and apply the saved model later to new data using the `apply`

command.

... | apply TESTMODEL_BernoulliNB

You can inspect the model learned by BernoulliNB with the `summary`

command as well as view the class and log probability information as calculated by the dataset.

.... | summary My_Incremental_Model

**Syntax constraints**

- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated or not. The default value is`False`

, meaning it will not be incrementally updated. Choosing`partial_fit=True`

allows you to update an existing model using only new data without having to retrain it on the full training data set. - Using
`partial_fit=True`

on an existing model ignores the newly supplied parameters. The parameters supplied at model creation are used instead. If`partial_fit=False`

or`partial_fit`

is not specified (default is False), the model specified is created and replaces the pre-trained model if one exists. - If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. If`My_Incremental_Model`

exists and was trained using BernoulliNB, the command updates the existing model with the new input. If`My_Incremental_Model`

exists but was not trained by BernoulliNB, an error message displays.

**Example**

The following example uses BernoulliNB on a test set.

... | fit BernoulliNB type from * into TESTMODEL_BernoulliNB alpha=0.5 binarize=0 fit_prior=f

### DecisionTreeClassifier

The DecisionTreeClassifier algorithm uses the scikit-learn DecisionTreeClassifier estimator to fit a model to predict the value of categorical fields. For further information, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html.

**Parameters**

To specify the maximum depth of the tree to summarize, use the `limit`

argument. The default value for the `limit`

argument is 5.

... | summary model_DTC limit=10

**Syntax**

fit DecisionTreeClassifier <field_to_predict> from <explanatory_fields> [into <model_name>] [max_depth=<int>] [max_features=<str>] [min_samples_split=<int>] [max_leaf_nodes=<int>] [criterion=<gini|entropy>] [splitter=<best|random>] [random_state=<int>]

You can save DecisionTreeClassifier models by using the `into`

keyword and apply it to new data later by using the `apply`

command.

... | apply model_DTC

You can inspect the decision tree learned by DecisionTreeClassifier with the `summary`

command.

... | summary model_DTC

See a JSON representation of the tree by giving `json=t`

as an argument to the `summary`

command.

... | summary model_DTC json=t

**Example**

The following example uses DecisionTreeClassifier on a test set.

... | fit DecisionTreeClassifier SLA_violation from * into sla_model | ...

### GaussianNB

The GaussianNB algorithm uses the scikit-learn GaussianNB estimator to fit a model to predict the value of categorical fields, where the likelihood of explanatory variables is assumed to be Gaussian. GaussianNB is an implementation of Gaussian Naive Bayes classification algorithm. This algorithm supports incremental fit.

**Parameters**

- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated or not. This allows you to update an existing model using only new data without having to retrain it on the full training data set. - The
`partial_fit`

parameter default is False.

**Syntax**

fit GaussianNB <field_to_predict> from <explanatory_fields> [into <model name>] [partial_fit=<true|false>]

You can save GaussianNB models using the `into`

keyword and apply the saved model later to new data using the `apply`

command.

... | apply TESTMODEL_GaussianNB

You can inspect models learned by GaussianNB with the `summary`

command.

... | summary My_Incremental_Model

**Syntax constraints**

- If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. If`My_Incremental_Model`

exists and was trained using GaussianNB, the command updates the existing model with the new input. If`My_Incremental_Model`

exists but was not trained by GaussianNB, an error message is thrown. - If
`partial_fit=False`

or`partial_fit`

is not specified the model specified is created and replaces the pre-trained model if one exists.

**Example**

The following example uses GaussianNB on a test set.

... | fit GaussianNB species from * into TESTMODEL_GaussianNB

The following example includes the `partial_fit`

command.

| inputlookup iris.csv | fit GaussianNB species from * partial_fit=true into My_Incremental_Model

### GradientBoostingClassifier

This algorithm uses the GradientBoostingClassifier from scikit-learn to build a classification model by fitting regression trees on the negative gradient of a deviance loss function. For further information, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html.

**Syntax**

fit GradientBoostingClassifier <field_to_predict> from <explanatory_fields>[into <model name>] [loss=<deviance | exponential>] [max_features=<str>] [learning_rate =<float>] [min_weight_fraction_leaf=<float>] [n_estimators=<int>] [max_depth=<int>] [min_samples_split =<int>] [min_samples_leaf=<int>] [max_leaf_nodes=<int>] [random_state=<int>]

You can apply the saved model later to new data using the `apply`

command.

... | apply TESTMODEL_GradientBoostingClassifier

You can inspect features learned by GradientBoostingClassifier with the `summary`

command.

... | summary TESTMODEL_GradientBoostingClassifier

**Example**

The following example uses GradientBoostingClassifier on a test set.

... | fit GradientBoostingClassifier target from * into TESTMODEL_GradientBoostingClassifier

### LogisticRegression

The LogisticRegression algorithm uses the scikit-learn LogisticRegression estimator to fit a model to predict the value of categorical fields.

**Parameters**

- The
`fit_intercept`

parameter specifies whether the model includes an implicit intercept term. - The default value of the
`fit_intercept`

parameter is True. - The
`probabilities`

parameter specifies whether probabilities for each possible field value should be returned alongside the predicted value. - The default value of the
`probabilities`

parameter is False.

**Syntax**

fit LogisticRegression <field_to_predict> from <explanatory_fields> [into <model name>] [fit_intercept=<true|false>] [probabilities=<true|false>]

You can save LogisticRegression models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply sla_model

You can inspect the coefficients learned by LogisticRegression with the `summary`

command.

... | summary sla_model

**Example**

The following examples uses LogisticRegression on a test set.

... | fit LogisticRegression SLA_violation from IO_wait_time into sla_model | ...

### MLPClassifier

The MPLClassifier algorithm uses the scikit-learn Multi-layer Perceptron estimator for classification. MLPClassifier uses a feedforward artificial neural network model that trains using backpropagation. This algorithm supports incremental fit.

For descriptions of the `batch_size`

, `random_state`

and `max_iter`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html

Using the MLPClassifier algorithm requires running version 1.3 or above of the Python for Scientific Computing add-on.

**Parameters**

- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated on not. This allows you to update nan existing model using only new data without having to retrain it on the full training data set. - The
`partial_fit`

parameter default is False. - The
`hidden_layer_sizes`

parameter format (`int`

) varies based on the number of hidden layers in the data.

**Syntax**

fit MLPClassifier <field_to_predict> from <explanatory_fields> [into <model name>] [batch_size=<int>] [max_iter=<int>] [random_state=<int>] [hidden_layer_sizes=<int>-<int>-<int>] [activation=<str>] [solver=<str>] [learning_rate=<str>] [tol=<float>} {momentum=<float>]

You can save MLPClassifier models by using the `into`

keyword and apply it to new data later by using the `apply`

command.

You can inspect models learned by MLPClassifier with the `summary`

command.

... | summary My_Example_Model

**Syntax constraints**

- If
`My_Example_Model`

does not exist, the model is saved to it. - If
`My_Example_Model`

exists and was trained using MLPClassifier, the command updates the existing model with the new input. - If
`My_Example_Model`

exists but was not trained using MLPClassifier, an error message displays.

**Example**

The following example uses MLPClassifier on a test set.

... | inputlookup diabetes.csv | fit MLPClassifier response from * into MLP_example_model hidden_layer_sizes='100-100-80' |...

The following example includes the `partial_fit`

command.

| inputlookup iris.csv | fit MLPClassifier species from * partial_fit=true into My_Example_Model

### RandomForestClassifier

The RandomForestClassifier algorithm uses the scikit-learn RandomForestClassifier estimator to fit a model to predict the value of categorical fields.

For descriptions of the `n_estimators`

, `max_depth`

, `criterion`

, `random_state`

, `max_features`

, `min_samples_split`

, and `max_leaf_nodes`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.

**Syntax**

fit RandomForestClassifier <field_to_predict> from <explanatory_fields> [into <model name>] [n_estimators=<int>] [max_depth=<int>] [criterion=<gini | entropy>] [random_state=<int>] [max_features=<str>] [min_samples_split=<int>] [max_leaf_nodes=<int>]

You can save RandomForestClassifier models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply sla_model

You can list the features that were used to fit the model, as well as their relative importance or influence with the `summary`

command.

... | summary sla_model

**Example**

The following example uses RandomForestClassifier on a test set.

... | fit RandomForestClassifier SLA_violation from * into sla_model | ...

### SGDClassifier

The SGDClassifier algorithm uses the scikit-learn SGDClassifier estimator to fit a model to predict the value of categorical fields. This algorithm supports incremental fit.

**Parameters**

- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated or not. This allows you to update an existing model using only new data without having to retrain it on the full training data set. - The
`partial_fit`

parameter default is False. `n_iter=<int>`

is the number of passes over the training data also known as epochs. The default is 5. The number of iterations is set to 1 if using`partial_fit`

.- The
`loss=<hinge|log|modified_huber|squared_hinge|perceptron>`

parameter is the loss function to be used.- Defaults to
`hinge`

, which gives a linear SVM.

- Defaults to
- The
`log`

loss gives logistic regression, a probabilistic classifier. `modified_huber`

is another smooth loss that brings tolerance to outliers as well as probability estimates.`squared_hinge`

is like hinge but is quadratically penalized.`perceptron`

is the linear loss used by the perceptron algorithm.- The
`fit_intercept=<true|false>`

parameter specifies whether the intercept should be estimated or not. The default is True. `penalty=<l2|l1|elasticnet>`

is the penalty, also known as regularization term, to be used. The default is l2.`learning_rate=<constant|optimal|invscaling>`

is the learning rate.`constant`

: eta = eta0`optimal`

: eta = 1.0/(alpha * t)`invscaling`

: eta = eta0 / pow(t, power_t)- The default is
`invscaling`

`l1_ratio=<float>`

is the Elastic Net mixing parameter, with 0 <= l1_ratio <= 1 (default 0.15).- l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.

`alpha=<float>`

is the constant that multiplies the regularization term (default 0.0001). Also used to compute learning_rate when set to`optimal`

.`eta0=<float>`

is the initial learning rate. The default is 0.01.`power_t=<float>`

is the exponent for inverse scaling learning rate. The default is 0.25.`random_state=<int>`

is the seed of the pseudo random number generator to use when shuffling the data.

**Syntax**

fit SGDClassifier <field_to_predict> from <explanatory_fields> [into <model name>] [partial_fit=<true|false>] [loss=<hinge|log|modified_huber|squared_hinge|perceptron>] [fit_intercept=<true|false>] [random_state=<int>] [n_iter=<int>] [l1_ratio=<float>] [alpha=<float>] [eta0=<float>] [power_t=<float>] [penalty=<l1|l2|elasticnet>] [learning_rate=<constant|optimal|invscaling>]

You can save SGDClassifier models using the `into`

keyword and apply the saved model later to new data using the `apply`

command.

... | apply sla_model

You can inspect the model learned by SGDClassifier with the `summary`

command.

... | summary sla_model

**Syntax constraints**

- If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. - If
`My_Incremental_Model`

exists and was trained using SGDClassifier, the command updates the existing model with the new input. - If
`My_Incremental_Model`

exists but was not trained by SGDClassifier, an error displays. - Using
`partial_fit=true`

on an existing model ignores the newly supplied parameters. The parameters supplied at model creation are used instead. - If
`partial_fit=false`

or`partial_fit`

is not specified the model specified is created and replaces the pre-trained model if one exists.

**Example**

The following example uses SGDClassifier on a test set.

... | fit SGDClassifier SLA_violation from * into sla_model

The following example includes the `partial_fit=<true|false>`

command.

partial_fit | inputlookup iris.csv | fit SGDClassifier species from * partial_fit=true into My_Incremental_Model

### SVM

The SVM algorithm uses the scikit-learn kernel-based SVC estimator to fit a model to predict the value of categorical fields. It uses the radial basis function (rbf) kernel by default. For descriptions of the `C`

and `gamma`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

Kernel-based methods such as the scikit-learn SVC tend to work best when the data is scaled, for example, using our StandardScaler algorithm:
`| fit StandardScaler `

. For details, see ''A Practical Guide to Support Vector Classification'' at https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

**Parameters**

- The
`gamma`

parameter controls the width of the rbf kernel. The default value is`1 /number of fields`

. - The
`C`

parameter controls the degree of regularization when fitting the model. The default value is 1.0.

**Syntax**

fit SVM <field_to_predict> from <explanatory_fields> [into <model name>] [C=<float>] [gamma=<float>]

You can save SVM models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply sla_model

**Syntax constraints**

You cannot inspect the model learned by SVM with the `summary`

command.

**Example**

The following example uses SVM on a test set.

... | fit SVM SLA_violation from * into sla_model | ...

## Clustering Algorithms

Clustering is the grouping of data points. Results will vary depending upon the clustering algorithm used. Clustering algorithms differ in how they determine if data points are similar and should be grouped. For example, the K-means algorithm clusters based on points in space, whereas the DBSCAN algorithm clusters based on local density.

### BIRCH

The BIRCH algorithm uses the scikit-learn BIRCH clustering algorithm to divide data points into set of distinct clusters. The cluster for each event is set in a new field named `cluster`

. This algorithm supports incremental fit.

**Parameters**

- The
`k`

parameter specifies the number of clusters to divide the data into after the final clustering step, which treats the sub-clusters from the leaves of the CF tree as new samples.- By default, the cluster label field name is
`cluster`

. Change that behavior by using the`as`

keyword to specify a different field name.

- By default, the cluster label field name is
- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated on not. This allows you to update nan existing model using only new data without having to retrain it on the full training data set. - The
`partial_fit`

parameter default is False.

**Syntax**

fit BIRCH <fields> [into <model name>] [k=<int>][partial_fit=<true|false>] [into <model name>]

You can save BIRCH models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply BIRCH_model

**Syntax constraints**

- If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. - If
`My_Incremental_Model`

exists and was trained using BIRCH, the command updates the existing model with the new input. - If
`My_Incremental_Model`

exists but was not trained by BIRCH, an error message displays. - Using
`partial_fit=true`

on an existing model ignores the newly supplied parameters. The parameters supplied at model creation are used instead. - If
`partial_fit=false`

or`partial_fit`

is not specified the model specified is created and replaces the pre-trained model if one exists. - You cannot inspect the model learned by BIRCH with the
`summary`

command.

**Examples**

The following example uses BIRCH on a test set.

... | fit BIRCH * k=3 | stats count by cluster

The following example includes the `partial_fit`

command.

| inputlookup track_day.csv | fit BIRCH * k=6 partial_fit=true into My_Incremental_Model

### DBSCAN

The DBSCAN algorithm uses the scikit-learn DBSCAN clustering algorithm to divide a result set into distinct clusters. The cluster for each event is set in a new field named `cluster`

. DBSCAN is distinct from K-Means in that it clusters results based on local density, and uncovers a variable number of clusters, whereas K-Means finds a precise number of clusters. For example, `k=5`

finds 5 clusters.

**Parameters**

- The
`eps`

parameter specifies the maximum distance between two samples for them to be considered in the same cluster.- By default, the cluster label field name is
`cluster`

. Change that behavior by using the`as`

keyword to specify a different field name.

- By default, the cluster label field name is
- The
`min_samples`

parameter defines the number of samples, or the total weight, in a neighborhood for a point to be considered as a core point - including the point itself. You can choose the`min_samples`

parameter's best value based on preference for cluster density or noise in your dataset. - The
`min_samples`

parameter is optional. - The
`min_samples`

default value is 5. - The minimum value for the
`min_samples`

parameter is 3. - If
`min_samples=8`

you need at least 8 data points to form a dense cluster.

If you choose the `min_samples`

parameter's best value based on noise in your dataset, it's recommended to have a larger data set to pull from.

**Syntax**

| fit DBSCAN <fields> [eps=<number>] [min_samples=<integer>]

**Syntax constraints**

You cannot save DBSCAN models using the `into`

keyword. To predict cluster assignments for future data, combine the DBSCAN algorithm with any classifier algorithm. For example, first cluster the data using DBSCAN, then fit RandomForestClassifier to predict the cluster.

**Examples**

The following example uses DBSCAN without the `min_samples`

parameter.

... | fit DBSCAN * | stats count by cluster

The following example uses DBSCAN with the `min_samples`

parameter.

...| inputlookup track_day.csv | fit DBSCAN eps=0.5 min_samples=1000 speed | table speed cluster

### K-means

K-means clustering is a type of unsupervised learning. It is a clustering algorithm that groups similar data points, with the number of groups represented by the variable `k`

. The K-means algorithm uses the scikit-learn K-means implementation. The cluster for each event is set in a new field named `cluster`

. Use the K-means algorithm when you have unlabeled data and have at least approximate knowledge of the total number of groups into which the data can be divided.

Using the K-means algorithm has the following advantages:

- Computationally faster than most other clustering algorithms.
- Simple algorithm to explain and understand.
- Normally produces tighter clusters than hierarchical clustering.

Using the K-means algorithm has the following disadvantages:

- Difficult to determine optimal or true value of
`k`

. See X-means. - Sensitive to scaling. See StandardScaler.
- Each clustering may be slightly different, unless you specify the
`random_state`

parameter. - Does not work well with clusters of different sizes and density.

For descriptions of default value of `K`

, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

**Parameters**

The `k`

parameter specifies the number of clusters to divide the data into. By default, the cluster label field name is `cluster`

. Change that behavior by using the `as`

keyword to specify a different field name.

**Syntax**

fit KMeans <fields> [into <model name>] [k=<int>] [random_state=<int>]

You can save K-means models using the `into`

keyword when using the `fit`

command.

You can apply the model to new data using the `apply`

command.

... | apply cluster_model

You can inspect the model using the `summary`

command.

... | summary cluster_model

**Example**

The following example uses K-means on a test set.

... | fit KMeans * k=3 | stats count by cluster

### SpectralClustering

The SpectralClustering algorithm uses the scikit-learn SpectralClustering clustering algorithm to divide a result set into set of distinct clusters. SpectralClustering first transforms the input data using the Radial Basis Function (rbf) kernel, and then performs K-Means clustering on the result. Consequently, SpectralClustering can learn clusters with a non-convex shape. The cluster for each event is set in a new field named `cluster`

.

**Parameters**

The `k`

parameter specifies the number of clusters to divide the data into after kernel step. By default, the cluster label field name is `cluster`

. Change that behavior by using the `as`

keyword to specify a different field name.

**Syntax**

fit SpectralClustering <fields> [k=<int>] [gamma=<float>] [random_state=<int>]

**Syntax constraints**

You cannot save SpectralClustering models using the `into`

keyword. If you want to be able to predict cluster assignments for future data, you can combine the SpectralClustering algorithm with any clustering algorithm. For example, first cluster the data using SpectralClustering, then fit a classifier to predict the cluster using RandomForestClassifier.

**Example**

The following example uses SpectralClustering on a test set.

... | fit SpectralClustering * k=3 | stats count by cluster

### X-means

Use the X-means algorithm when you have unlabeled data and no prior knowledge of the total number of labels into which that data could be divided. The X-means clustering algorithm is an extended K-means that automatically determines the number of clusters based on Bayesian Information Criterion (BIC) scores. Starting with a single cluster, the X-means algorithm goes into action after each run of K-means, making local decisions about which subset of the current centroids should split themselves in order to fit the data better.

Using the X-means algorithm has the following advantages:

- Eliminates the requirement of having to provide the value of
`k`

. - Normally produces tighter clusters than hierarchical clustering.

Using the X-means algorithm has the following disadvantages:

- Sensitive to scaling. See StandardScaler.
- Different initializations might result in different final clusters.
- Does not work well with clusters of different sizes and density.

**Parameters**

- The
`k`

is the total number of labels/clusters in the data. - The splitting decision is done by computing the BIC.
- The cluster for each event is set in a new field named cluster, and the total number of clusters is set in a new field named
`n_clusters`

. - By default, the cluster label field name is
`cluster`

. Change that behavior by using the`as`

keyword to specify a different field name.

**Syntax**

fit XMeans <fields> [into <model name>]

You can apply new data to the saved X-means model using the `apply`

command.

... | apply cluster_model

You can save X-means models using the `into`

command. You can inspect the model learned by X-means with the `summary`

command.

...| summary cluster_model

**Example**

The following example uses X-means on a test set.

... | fit XMeans * | stats count by cluster

## Cross-validation

Cross-validation assesses how well a statistical model generalizes on an independent dataset. Cross-validation tells you how well your machine learning model is expected to perform on data that it has not been trained on. There are many types of cross-validation, but K-fold cross-validation (`kfold_cv`

) is one of the most common.

Cross-validation is typically used for the following machine learning scenarios:

- Comparing two or more algorithms against each other for selecting the best choice on a particular dataset.
- Comparing different choices of hyper-parameters on the same algorithm for choosing the best hyper-parameters for a particular dataset.
- An improved method over a train/test split for quantifying model generalization.

Cross-validation is **not** well suited for time-series charts:

- In situations where the data is ordered such as time-series, cross-validation is not well suited because the training data is shuffled. In these situations, other methods such as Forward Chaining are more suitable.
- The most straightforward implementation is to wrap sklearn's Time Series Split. Learn more here: https://en.wikipedia.org/wiki/Forward_chaining

### K-fold cross-validation

In the `kfold_cv`

parameter, the training set is randomly partitioned into k equal-sized subsamples. Then, each sub-sample takes a turn at becoming the validation (test) set, predicted by the other k-1 training sets. Each sample is used exactly once in the validation set, and the variance of the resulting estimate is reduced as k is increased. The disadvantage of the `kfold_cv`

parameter is that k different models have to be trained, leading to long execution times for large datasets and complex models.

The scores obtained from K-fold cross-validation are generally a less biased and less optimistic estimate of the model performance than a standard training and testing split.

You can obtain k performance metrics, one for each training and testing split. These k performance metrics can then be averaged to obtain a single estimate of how well the model generalizes on unseen data.

**Syntax**

The `kfold_cv`

parameter is applicable to to all classification and regression algorithms, and you can append the command to the end of an SPL search.

Here `kfold_cv=<int>`

specifies that `k=<int>`

folds is used. When you specify a classification algorithm, stratified k-fold is used instead of k-fold. In stratified k-fold, each fold contains approximately the same percentage of samples for each class.

..| fit <classification | regression algo> <targetVariable> from <featureVariables> [options] kfold_cv=<int>

The `kfold_cv`

parameter cannot be used when saving a model.

**Output**
The `kfold_cv`

parameter returns performance metrics on each fold using the same model specified in the SPL - including algorithm and hyper parameters. Its only function is to give you insight into how well you model generalizes. It does not perform any model selection or hyper parameter tuning.

**Examples**

The first example shows the `kfold_cv`

parameter used in classification. Where the output is a set of metrics for each fold including accuracy, f1_weighted, precision_weighted, and recall_weighted.

This second example shows the `kfold_cv`

parameter used in classification. Where the output is a set of metrics for each the neg_mean_squared_error and r^2 folds.

## Feature Extraction

Feature extraction algorithms transform fields for better prediction accuracy.

### FieldSelector

The FieldSelector algorithm uses the scikit-learn GenericUnivariateSelect to select the best predictor fields based on univariate statistical tests. For descriptions of the `mode`

and `param`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html.

**Parameters**

The `type`

parameter specifies if the field to predict is categorical or numeric.

**Syntax**

fit FieldSelector <field_to_predict> from <explanatory_fields> [into <model name>] [type=<categorical, numeric>] [mode=<k_best, fpr, fdr, fwe, percentile>] [param=<int>]

You can save FieldSelector models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply sla_model

You can inspect the model learned by FieldSelector with the `summary`

command.

| summary sla_model

**Example**

The following example uses FieldSelector on a test set.

... | fit FieldSelector type=categorical SLA_violation from * into sla_model | ...

### HashingVectorizer

The HashingVectorizer algorithm converts text documents to a matrix of token occurrences. It uses a feature hashing strategy to allow for hash collisions when measuring the occurrence of tokens. It is a stateless transformer, meaning that it does not require building a vocabulary of the seen tokens. This reduces the memory footprint and allows for larger feature spaces.

HashingVectorizer is comparable with the TFIDF algorithm, as they share many of the same parameters. However HashingVectorizer is a better option for building models with large text fields provided you do not need to know term frequencies, and only want outcomes.

For descriptions of the `max_features`

, `max_df`

, `min_df`

, `ngram_range`

, `analyzer`

, `norm`

, and `token_pattern`

parameters, see the scikit-learn documentation at https://scikit-learn.org/0.19/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html

**Parameters**

- The
`reduce`

parameter is either`True`

or`False`

and determines whether or not to reduce the output to a smaller dimension using TruncatedSVD. - The
`reduce`

parameter default is True. - The
`k=<int>`

parameter sets the number of dimensions to reduce when the`reduce`

parameter is set to`true`

. Default is 100. - The default for the
`max_features`

parameter is 10,000. - The
`n_iters`

parameter specifies the number of iterations to to use when performing dimensionality reduction. This is only used when the`reduce`

parameter is set to`True`

. Default is 5.

**Syntax**

fit HashingVectorizer <field_to_convert> [max_features=<int>] [n_iters=<int>] [reduce=<bool>] [k=<int>] [ngram_range=<int>-<int>] [analyzer=<str>] [norm=<str>] [token_pattern=<str>] [stop_words=english]

**Syntax constraints**

HashingVectorizer does not support saving models, incremental fit, or K-fold cross validation.

**Example**

The following example uses HashingVectorizer to hash the text dataset and applies KMeans clustering (where `k=3`

) on the hashed fields.

| inputlookup authorization.csv | fit HashingVectorizer Logs ngram_range=1-2 k=50 stop_words=english | fit KMeans Logs_hashed* k=3 | fields cluster* Logs | sample 5 by cluster | sort by cluster

### ICA

ICA (Independent component analysis) separates a multivariate signal into additive sub-components that are maximally independent. Typically, ICA is not used for separating superimposed signals, but for reducing dimensionality. The ICA model does not include a noise term for the model to be correct, meaning whitening must be applied. Whitening can be done internally using the whiten argument, or manually using one of the PCA variants.

**Parameters**

- The
`n_components`

parameters determines the number of components ICA uses. - The
`n_components`

parameter is optional. - The
`n_components`

parameter default is`None`

. If`None`

is selected, all components are used. - Use the
`algorithm`

parameter to apply`parallel`

or`deflation`

algorithm for FastICA. - The the
`algorithm`

parameter default is`algorithm='parallel'`

. - Use the
`whiten`

parameter to set a noise term. - The
`whiten`

parameter is optional. - If the
`whiten`

parameter is`False`

no whitening is performed. - The
`whiten`

parameter default is`True`

. - The
`max_iter`

parameter determines the maximum number of iterations during the running of the`fit`

command. - The
`max_iter`

parameter is optional. - The
`max_iter`

parameter default is 200. - The
`fun`

parameter determines the functional form of the G function used in the approximation to neg-entropy. - The
`fun`

parameter is optional. - The
`fun`

parameter default is`logcosh`

. Other options for this parameter are`exp`

or`cube`

. - The
`tol`

parameter sets the tolerance on update at each iteration. - The
`tol`

parameter is optional. - The
`tol`

parameter default is 0.0001 . - The
`random_state`

parameter sets the seed value used by the random number generator. - The
`random_state`

parameter default is`None`

. - If
`random_state=None`

then a random seed value is used.

**Syntax**

fit ICA n_components=<int>, algorithm=<"parallel"|"deflation">, whiten=<bool>, fun=<"logcosh"|"exp"|"cube">, max_iter=<int>, tol=<float>, random_state=<int> <explanatory_fields> [into <model name>]

You can save ICA models using the `into`

keyword and apply new data later using the `apply`

command.

**Syntax constraints**

You cannot inspect the model learned by ICA with the `summary`

command.

**Example**

The following example shows how ICA is able to find the two original sources of data from two measurements that have mixes of both. As a comparison, PCA is used to show the difference between the two – PCA is not able to identify the original sources.

| makeresults count=2 | streamstats count as count | eval time=case(count=2,relative_time(now(),"+2d"),count=1,now()) | makecontinuous time span=15m | eval _time=time | eval s1 = sin(2*time) | eval s2 = sin(4*time) | eval m1 = 1.5*s1 + .5*s2, m2 = .1*s1 + s2 | fit ICA m1, m2 n_components=2 as IC | fit PCA m1, m2 k=2 as PC | fields _time, * | fields - count, time

### KernelPCA

The KernelPCA algorithm uses the scikit-learn KernelPCA to reduce the number of fields by extracting uncorrelated new features out of data. The difference between KernelPCA and PCA is the use of kernels in the former, which helps with finding nonlinear dependencies among the fields. Currently, KernelPCA only supports the Radial Basis Function (rbf) kernel.

For descriptions of the `gamma`

, `degree`

, `tolerance`

, and `max_iteration`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html.

Kernel-based methods such as KernelPCA tend to work best when the data is scaled, for example, using our StandardScaler algorithm: `| fit StandardScaler `

. For details, see ''A Practical Guide to Support Vector Classification'' at https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

**Parameters**

The `k`

parameter specifies the number of features to be extracted from the data. The other parameters are for fine tuning of the kernel.

**Syntax**

fit KernelPCA <fields> [into <model name>] [degree=<int>] [k=<int>] [gamma=<int>] [tolerance=<int>] [max_iteration=<int>]

You can save KernelPCA models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply user_feedback_model

**Syntax constraints**

You cannot inspect the model learned by KernelPCA with the `summary`

command.

**Example**

The following example uses KernelPCA on a test set.

... | fit KernelPCA * k=3 gamma=0.001 | ...

### PCA

The Principal Component Analysis (PCA) algorithm uses the scikit-learn PCA algorithm to reduce the number of fields by extracting new, uncorrelated features out of the data.

**Parameters**

- The
`k`

parameter specifies the number of features to be extracted from the data. - The
`variance`

parameter is short for percentage variance ratio explained. This parameter determines the percentage of variance ratio explained in the principal components of the PCA. It computes the number of principal components dynamically by preserving the specified variance ratio. - The
`variance`

parameter defaults to 1 if k is not provided. - The
`variance`

parameter can take a value between 0 and 1. - The
`component name`

parameter represents the name of the selected components from the value specified in`n_components`

. - The
`explained_variance`

parameter measures the proportion to which the principal component accounts for dispersion of a given dataset. A higher value denotes a higher variation. - The
`explained_variance_ratio`

parameter is the percentage of variance explained by each of the selected components. - The
`singular_values`

parameter represents the singular values corresponding to each of the selected components. Singular values are equal to the 2-norms of the`n_components`

variables in the lower-dimensional space.

**Syntax**

fit PCA <fields> [into <model name>] [k=<int>] [variance=<float>]

You can save PCA models using the `into`

keyword and apply new data later using the `apply`

command.

...into example_hard_drives_PCA_2 | apply example_hard_drives_PCA_2

You can inspect the model learned by PCA with the `summary`

command.

| summary example_hard_drives_PCA_2

**Syntax constraints**

The `variance`

parameter and `k`

parameter cannot be used together. They are mutually exclusive.

**Examples**

The following example uses PCA on a test set.

| fit PCA "SS_SMART_1_Raw", "SS_SMART_2_Raw", "SS_SMART_3_Raw", "SS_SMART_4_Raw", "SS_SMART_5_Raw" k=2 into example_hard_drives_PCA_2

The following example includes the `variance`

parameter. The value `variance=0.5`

tells the algorithm to choose as many principal components for the data set until able to explain 50% of the variance in the original dataset.

| fit PCA "SS_SMART_1_Raw", "SS_SMART_2_Raw", "SS_SMART_3_Raw", "SS_SMART_4_Raw", "SS_SMART_5_Raw" variance=0.50 into example_hard_drives_PCA_2

### TFIDF

The TFIDF algorithm uses the scikit-learn TfidfVectorizer to convert raw text data into a matrix making it possible to use other machine learning estimators on the data. For descriptions of the `max_features`

, `max_df`

, `min_df`

, `ngram_range`

, `analyzer`

, `norm`

, and `token_pattern`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html.

TFIDF uses memory to create a dictionary of all terms including ngrams and words, and expands the Splunk search events with additional fields per event. If you are concerned with memory limits, consider using the HashingVectorizer algorithm.

**Parameters**

The default for `max_features`

is 100.

To configure the algorithm to ignore common English words (for example, "the", "it", "at", and "that"), set `stop_words`

to `english`

. For other languages (for example, machine language) you can ignore the common words by setting `max_df`

to a value greater than or equal to 0.7 and less than 1.0.

**Syntax**

fit TFIDF <field_to_convert> [into <model name>] [max_features=<int>] [max_df=<int>] [min_df=<int>] [ngram_range=<int>-<int>] [analyzer=<str>] [norm=<str>] [token_pattern=<str>] [stop_words=english]

You can save TFIDF models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply user_feedback_model

**Syntax constraints**

You cannot inspect the model learned by TFIDF with the `summary`

command.

**Example**

The following example uses TFIDF to convert the text dataset to a matrix of TF-IDF features and then applies KMeans clustering (where `k=3`

) on the matrix.

| inputlookup authorization.csv | fit TFIDF Logs ngram_range=1-2 ngram_range=1-2 max_df=0.6 min_df=0.2 stop_words=english | fit KMeans Logs_tfidf* k=3 | fields cluster Logs | sample 6 by cluster | sort by cluster

## Preprocessing (Prepare Data)

Preprocessing algorithms are used for preparing data. Other algorithms can also be used for preprocessing that may not be organized under this section. For example, PCA can be used for both Feature Extraction *and* Preprocessing.

### Imputer

The Imputer algorithm is a preprocessing step wherein missing data is replaced with substitute values. The substitute values can be estimated, or based on other statistics or values in the dataset. To use Imputer, the user passes in the names of the fields to impute, along with arguments specifying the imputation strategy, and the values representing missing data. Imputer then adds new imputed versions of those fields to the data, which are copies of the original fields, except that their missing values are replaced by values computed according to the imputation strategy.

**Parameters**

- Available imputation strategies include mean, median, most frequent, and field. The default strategy is
`mean`

. - All but the
`field`

parameter require numeric data. The`field`

strategy accepts categorical data.

**Syntax**

.. | fit Imputer <field>* [as <field prefix>] [missing_values=<"NaN"|integer>] [strategy=<mean|median|most_frequent>] [into <model name>]

You can inspect the value (mean, median, or mode) that was substituted for missing values by Imputer with the `summary`

command.

... | summary <imputer model name>

You can save Imputer models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply <imputer model name>

**Example**

The following example uses Imputer on a test set.

| inputlookup server_power.csv | eval ac_power_missing=if(random() % 3 = 0, null, ac_power) | fields - ac_power | fit Imputer ac_power_missing | eval imputed=if(isnull(ac_power_missing), 1, 0) | eval ac_power_imputed=round(Imputed_ac_power_missing, 1) | fields - ac_power_missing, Imputed_ac_power_missing

### RobustScaler

The RobustScaler algorithm uses the scikit-learn RobustScaler algorithm to standardize data fields by scaling their median and interquartile range to 0 and 1, respectively. It is very similar to the StandardScaler algorithm, in that it helps avoid dominance of one or more fields over others in subsequent machine learning algorithms, and is practically required for some algorithms, such as KernelPCA and SVM. The main difference between StandardScaler and RobustScaler is that RobustScaler is less sensitive to outliers.

**Parameters**

The `with_centering`

and `with_scaling`

parameters specify if the fields should be standardized with respect to their median and interquartile range.

**Syntax**

fit RobustScaler <fields> [into <model name>] [with_centering=<true|false>] [with_scaling=<true|false>]

You can save RobustScaler models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply scaling_model

You can inspect the statistics extracted by RobustScaler with the `summary`

command.

... | summary scaling_model

**Syntax constraints**

RobustScaler does not support incremental fit.

**Example**

The following example uses RobustScaler on a test set.

... | fit RobustScaler * | ...

### StandardScaler

The StandardScaler algorithm uses the scikit-learn StandardScaler algorithm to standardize data fields by scaling their mean and standard deviation to 0 and 1, respectively. This preprocessing step helps to avoid dominance of one or more fields over others in subsequent machine learning algorithms. This step is practically required for some algorithms, such as KernelPCA and SVM. This algorithm supports incremental fit.

**Parameters**

- The
`with_mean`

and`with_std`

parameters specify if the fields should be standardized with respect to their mean and standard deviation. - The
`partial_fit`

parameter controls whether an existing model should be incrementally updated or not. This allows you to update an existing model using only new data without having to retrain it on the full training data set. The default is False.

**Syntax**

fit StandardScaler <fields> [into <model name>] [with_mean=<true|false>] [with_std=<true|false>] [partial_fit=<true|false>]

You can save StandardScaler models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply scaling_model

You can inspect the statistics extracted by StandardScaler with the `summary`

command.

...| summary scaling_model

**Syntax constraints**

- Using
`partial_fit=true`

on an existing model ignores the newly supplied parameters. The parameters supplied at model creation are used instead. If`partial_fit=false`

or`partial_fit`

is not specified (default is false), the model specified is created and replaces the pre-trained model if one exists. - If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. - If
`My_Incremental_Model`

exists and was trained using StandardScaler, the command updates the existing model with the new input. - If
`My_Incremental_Model`

exists but was not trained by StandardScaler, an error message is thrown.

**Examples**

The following example uses StandardScaler on a test set.

... | fit StandardScaler * | ...

The following example includes the `partial_fit`

parameter.

| inputlookup track_day.csv | fit StandardScaler "batteryVoltage", "engineCoolantTemperature", "engineSpeed" partial_fit=true into My_Incremental_Model

## Regressors

Regressor algorithms predict the value of a numeric field.

The `kfold`

cross-validation command can be used with all Regressor algorithms. Learn more here.

### DecisionTreeRegressor

The DecisionTreeRegressor algorithm uses the scikit-learn DecisionTreeRegressor estimator to fit a model to predict the value of numeric fields. For descriptions of the `max_depth`

, `random_state`

, `max_features`

, `min_samples_split`

, `max_leaf_nodes`

, and `splitter`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html.

**Parameters**

To specify the maximum depth of the tree to summarize, use the `limit`

argument. The default value for the `limit`

argument is 5.

| summary model_DTC limit=10

**Syntax**

fit DecisionTreeRegressor <field_to_predict> from <explanatory_fields> [into <model_name>] [max_depth=<int>] [max_features=<str>] [min_samples_split=<int>] [random_state=<int>] [max_leaf_nodes=<int>] [splitter=<best|random>]

You can save DecisionTreeRegressor models using the `into`

keyword and apply it to new data later using the `apply`

command.

... | apply model_DTR

You can inspect the decision tree learned by DecisionTreeRegressor with the `summary`

command.

... | summary model_DTR

You can get a JSON representation of the tree by giving `json=t`

as an argument to the `summary`

command.

... | summary model_DTR json=t

**Example**

The following example uses DecisionTreeRegressor on a test set.

... | fit DecisionTreeRegressor temperature from date_month date_hour into temperature_model | ...

### ElasticNet

The ElasticNet algorithm uses the scikit-learn ElasticNet estimator to fit a model to predict the value of numeric fields. ElasticNet is a linear regression model that includes both L1 and L2 regularization and is a generalization of Lasso and Ridge.

For descriptions of the `fit_intercept`

, `normalize`

, `alpha`

, and `l1_ratio`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html.

**Syntax**

fit ElasticNet <field_to_predict> from <explanatory_fields> [into <model name>] [fit_intercept=<true|false>] [normalize=<true|false>] [alpha=<int>] [l1_ratio=<int>]

You can save ElasticNet models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can inspect the coefficients learned by ElasticNet with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses ElasticNet on a test set.

... | fit ElasticNet temperature from date_month date_hour normalize=true alpha=0.5 | ...

### GradientBoostingRegressor

This algorithm uses the GradientBoostingRegressor algorithm from scikit-learn to build a regression model by fitting regression trees on the negative gradient of a loss function. For further information see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html

**Syntax**

fit GradientBoostingRegressor <field_to_predict> from <explanatory_fields> [into <model_name>] [loss=<ls|lad|huber|quantile>] [max_features=<str>] [learning_rate=<float>] [min_weight_fraction_leaf=<float>] [alpha=<float>] [subsample=<float>] [n_estimators=<int>] [max_depth=<int>] [min_samples_split=<int>] [min_samples_leaf=<int>] [max_leaf_nodes=<int>] [random_state=<int>]

You can use the `apply`

method to apply the trained model to the new data.

...|apply temperature_model

You can inspect the features learned by GradientBoostingRegressor with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses the GradientBoostingRegressor algorithm to fit a model and saves that model as ` temperature_model`

.

... | fit GradientBoostingRegressor temperature from date_month date_hour into temperature_model | ...

### KernelRidge

The KernelRidge algorithm uses the scikit-learn KernelRidge algorithm to fit a model to predict numeric fields. This algorithm uses the radial basis function (rbf) kernel by default. For details, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html.

**Parameters**

The `gamma`

parameter controls the width of the rbf kernel. The default value is `1/ number of fields`

.

**Syntax**

fit KernelRidge <field_to_predict> from <explanatory_fields> [into <model_name>] [gamma=<float>]

You can save KernelRidge models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply sla_model

**Syntax constraints**

You cannot inspect the model learned by KernelRidge with the `summary`

command.

**Example**

The following example uses KernelRidge on a test set.

... | fit KernelRidge temperature from date_month date_hour into temperature_model | ...

### Lasso

The Lasso algorithm uses the scikit-learn Lasso estimator to fit a model to predict the value of numeric fields. Lasso is like LinearRegression, but it uses L1 regularization to learn a linear models with fewer coefficients and smaller coefficients. Lasso models are consequently more robust to noise and resilient against overfitting.

For descriptions of the `alpha`

, `fit_intercept`

, and `normalize`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html.

**Parameters**

- The
`alpha`

parameter controls the degree of L1 regularization. - The
`fit_intercept`

parameter specifies whether the model should include an implicit intercept term. The default value is True.

**Syntax**

fit Lasso <field_to_predict> from <explanatory_fields> [into <model name>] [alpha=<float>] [fit_intercept=<true|false>] [normalize=<true|false>]

You can save Lasso models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can inspect the coefficients learned by Lasso with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses Lasso on a test set.

... | fit Lasso temperature from date_month date_hour | ...

### LinearRegression

The LinearRegression algorithm uses the scikit-learn LinearRegression estimator to fit a model to predict the value of numeric fields.

**Parameters**

The `fit_intercept`

parameter specifies whether the model should include an implicit intercept term. The default value is True.

**Syntax**

fit LinearRegression <field_to_predict> from <explanatory_fields> [into <model name> [fit_intercept=<true|false>] [normalize=<true|false>]

You can save LinearRegression models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can inspect the coefficients learned by LinearRegression with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses LinearRegression on a test set.

... | fit LinearRegression temperature from date_month date_hour into temperature_model | ..

### RandomForestRegressor

The RandomForestRegressor algorithm uses the scikit-learn RandomForestRegressor estimator to fit a model to predict the value of numeric fields. For descriptions of the `n_estimators`

, `random_state`

, `max_depth`

, `max_features`

, `min_samples_split`

, and `max_leaf_nodes`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html.

**Syntax**

fit RandomForestRegressor <field_to_predict> from <explanatory_fields> [into <model name>] [n_estimators=<int>] [max_depth=<int>] [random_state=<int>] [max_features=<str>] [min_samples_split=<int>] [max_leaf_nodes=<int>]

You can save RandomForestRegressor models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can list the features that were used to fit the model, as well as their relative importance or influence with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses RandomForestRegressor on a test set.

... | fit RandomForestRegressor temperature from date_month date_hour into temperature_model | ...

### Ridge

The Ridge algorithm uses the scikit-learn Ridge estimator to fit a model to predict the value of numeric fields. Ridge is like LinearRegression, but it uses L2 regularization to learn a linear models with smaller coefficients, making the algorithm more robust to collinearity. For descriptions of the `fit_intercept`

, `normalize`

, and `alpha`

parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html.

**Parameters**

The `alpha`

parameter specifies the degree of regularization. The default value is 1.0.

**Syntax**

fit Ridge <field_to_predict> from <explanatory_fields> [into <model name>] [fit_intercept=<true|false>] [normalize=<true|false>] [alpha=<int>]

You can save Ridge models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can inspect the coefficients learned by Ridge with the `summary`

command.

... | summary temperature_model

**Example**

The following example uses Ridge on a test set.

... | fit Ridge temperature from date_month date_hour normalize=true alpha=0.5 | ...

### SGDRegressor

The SGDRegressor algorithm uses the scikit-learn SGDRegressor estimator to fit a model to predict the value of numeric fields. This algorithm supports incremental fit.

**Parameters**

- The
`partial_fit`

parameter controls whether an existing model should be incrementally updated or not. This allows you to update an existing model using only new data without having to retrain it on the full training data set. The default is False. - The
`fit_intercept=<true|false>`

parameter determines whether the intercept should be estimated or not. - The
`fit_intercept=<true|false>`

parameter default is True. - The
`n_iter=<int>`

parameter is the number of passes over the training data also known as epochs. The default is 5.- The number of iterations is set to 1 if using
`partial_fit`

.

- The number of iterations is set to 1 if using
- The
`penalty=<l2|l1|elasticnet>`

parameter set the penalty or regularization term to be used. The default is l2. - The
`learning_rate=<constant|optimal|invscaling>`

parameter is the learning rate.- constant: eta = eta0
- optimal: eta = 1.0/(alpha * t)
- invscaling: eta = eta0 / pow(t, power_t)
- default is
`invscaling`

.

- The
`l1_ratio=<float>`

parameter is the Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. Default is 0.15.- l1_ratio=0 corresponds to L2 penalty
- l1_ratio=1 to L1

- The
`alpha=<float>`

parameter is the constant that multiplies the regularization term. Default is 0.0001.- Also used to compute
`learning_rate`

when set to Optimal.

- Also used to compute
- The
`eta0=<float>`

parameter is the initial learning rate. Default is 0.01. - The
`power_t=<float>`

parameter is the exponent for inverse scaling learning rate. Default is 0.25. - The
`random_state=<int>`

parameter is the seed of the pseudo random number generator to use when shuffling the data.

**Syntax**

fit SGDRegressor <field_to_predict> from <explanatory_fields> [into <model name>] [partial_fit=<true|false>] [fit_intercept=<true|false>] [random_state=<int>] [n_iter=<int>] [l1_ratio=<float>] [alpha=<float>] [eta0=<float>] [power_t=<float>] [penalty=<l1|l2|elasticnet>] [learning_rate=<constant|optimal|invscaling>]

You can save SGDRegressor models using the `into`

keyword and apply new data later using the `apply`

command.

... | apply temperature_model

You can inspect the coefficients learned by SGDRegressor with the `summary`

command.

... | summary temperature_model

**Syntax constraints**

- If
`My_Incremental_Model`

does not exist, the command saves the model data under the model name`My_Incremental_Model`

. - If
`My_Incremental_Model`

exists and was trained using SGDRegressor, the command updates the existing model with the new input. - If
`My_Incremental_Model`

exists but was not trained by SGDRegressor, an error message displays. - Using
`partial_fit=true`

on an existing model ignores the newly supplied parameters. The parameters supplied at model creation are used instead. - If
`partial_fit=false`

or`partial_fit`

is not specified the model specified is created and replaces the pre-trained model if one exists.

**Examples**

The following example uses SGDRegressor on a test set.

... | fit SGDRegressor temperature from date_month date_hour into temperature_model | ...

The following example includes the`partial_fit`

parameter.

| inputlookup server_power.csv | fit SGDRegressor "ac_power" from "total-cpu-utilization" "total-disk-accesses" partial_fit=true into My_Incremental_Model

## Time Series Analysis

Forecasting algorithms, also known as time series analysis, provide methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data, and forecast its future values.

### ARIMA

The Autoregressive Integrated Moving Average (ARIMA) algorithm uses the StatsModels ARIMA algorithm to fit a model on a time series for better understanding and/or forecasting its future values. An ARIMA model can consist of autoregressive terms, moving average terms, and differencing operations. The autoregressive terms express the dependency of the current value of time series to its previous ones.

The moving average terms, also called random shocks or white noise, model the effect of previous forecast errors on the current value. If the time series is non-stationary, differencing operations are used to make it stationary. A stationary process is a stochastic process in that its probability distribution does not change over time.

See the StatsModels documentation at http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.arima_model.ARIMA.html for more information.

It is highly recommended to send the time series through timechart before sending it into ARIMA to avoid non-uniform sampling time. If `_time`

is not to be specified, using timechart is not necessary.

**Parameters**

- The time series should not have any gaps or missing data otherwise ARIMA will complain. If there are missing samples in the data, using a bigger span in timechart or using streamstats to fill in the gaps with average values can do the trick.
- When chaining ARIMA output to another algorithm (i.e. ARIMA itself), keep in mind the length of the data is the length of the original data +
`forecast_k`

. If you want to maintain the`holdback`

position, you need to add the number in`forecast_k`

to your`holdback`

value. - ARIMA requires the
`order`

parameter to be specified at fitting time. The`order`

parameter needs three values:- Number of autoregressive (AR) parameters
- Number of differencing operations (D)
- Number of moving average (MA) Parameters

- The
`forecast_k=<int>`

parameter tells ARIMA how many points into the future should be forecasted. If`_time`

is specified during fitting along with the`field_to_forecast`

, ARIMA will also generate the timestamps for forecasted values. By default,`forecast_k`

is zero. - The
`conf_interval=<1..99>`

parameter is the confidence interval in percentage around forecasted values. By default it is set to 95%. - The
`holdback=<int>`

parameter is the number of data points held back from the ARIMA model. This is useful for comparing the forecast against known data points. By default, holdback is zero.

**Syntax**

fit ARIMA [_time] <field_to_forecast> order=<int>-<int>-<int> [forecast_k=<int>] [conf_interval=<int>] [holdback=<int>]

**Syntax constraints**

- ARIMA supports one time series at a time.
- ARIMA models cannot be saved and used at a later time in the current version.
- Scoring metric values are based on all data and not on the
`holdback`

period data.

**Example**

The following example uses ARIMA on a test set.

... | fit ARIMA Voltage order=4-0-1 holdback=10 forecast_k=10

### StateSpaceForecast

StateSpaceForecast is a forecasting algorithm for time series in MLTK. It is based on Kalman filters. The algorithm supports incremental fit.

Advantages of StateSpaceForecast over ARIMA include:

- Persists models created using the
`fit`

command that can then be used with`apply`

. - A
`specialdays`

field allows you to account for the effects of a specified list of special days. - It is automatic in that you do no need to choose parameters or mode.
- Supports multivariate forecasting.

**Parameters**

- By default the historical data results from running the
`fit`

command are not shown. To modify this behavior set`output_fit=True`

. - Use the
`target`

field to specify fields from which to forecast using historical data and other values. - The
`target`

field is a comma-separated list of fields that can be univariate or multivariate. These fields must be specified during the`fit`

process.- Optionally use the
`target`

field to fit multiple fields during the`fit`

process but apply only a selection of those target fields during the`apply`

process.

- Optionally use the
- If the
`target`

field is not specified, then all fields will be forecast together using historical data. - The
`specialdays`

field specifies the field that indicates effects due to special days such as holidays. - The
`specialdays`

field values must be numeric and are typically 0 and 1, with 1 indicating the existence of a special day effect. Null values are treated as 0. - The majority of use cases have no
`specialdays`

. Events that occur regularly and frequently such as weekends should not be treated as`specialdays`

. As best practice, use a minimum of two and a maximum of five instances of the`specialdays`

to capture events such as holiday sales. - * Use
`specialdays`

in the`apply`

step if it has been specified during`fit`

. The same field(s) must be assigned. - Use the
`period`

parameter to specify if your data has a known periodicity. - If the
`period`

parameter is not specified it is computed automatically. - Set
`period=1`

to treat the time series as non-periodic. - As with other MLTK algorithms, the
`partial_fit`

parameter controls whether a model should be incrementally updated or not. This allows you to update a model using only new data without having to retrain the model on the full dataset. - The default for
`partial_fit`

is False. - Use
`update_last`

to modify the behavior of`partial_fit`

- The default for
`update_last`

is False. - If
`partial_fit=True`

StateSpaceForecast will first update the model parameters and then predict. - If
`partial_fit=True`

and`update_last=True`

StateSpaceForecast will first predict and then update the model parameters. This allows you to review the forecast before running new data through. - The
`holdback=<int>`

parameter is the number of data points held back from training. This is useful for comparing the forecast against known data points. Default holdback value is 0. - If you want to maintain the
`holdback`

position, add the position number in`forecast_k`

to your`holdback`

value. - The
`forecast_k=<int>`

parameter tells StateSpaceForecast how many points into the future should be forecasted. If`_time`

is specified during fitting along with the`field_to_forecast`

, StateSpaceForecast also generates the timestamps for forecasted values. Default,`forecast_k`

value is 0. - The
`conf_interval=<1..99>`

parameter is the confidence interval in percentage around forecasted values. Input an integer between 1 and 99 where a larger number means a greater tolerance for forecast uncertainty. The default integer is 95. - The
`as`

field to gives aliases for forecasted fields. - In univariate cases the
`as`

field`field-list`

is a single field name. - In multivariate cases, the
`as`

field adheres to the following conventions:- The list must be in double quotes, separated by either spaces or commas.
- The aliases correspond to the original fields in the given order.
- The number of aliases can be smaller than the number of original fields.

- The
`summary`

command lists the names of the fields used in the`fit`

command step, the name of the`specialdays`

field, and the period.

**Syntax**

| fit StateSpaceForecast <fields> [from *] [specialdays=<field name>] [holdback=<int>] [forecast_k=<int>] [conf_interval=<float>] [period=<int>] [partial_fit=<true|false>] [update_last=<true|false>] [output_fit=<true|false>] [into <model name>] [as <field-list>]

You can apply the saved model to new data with the `apply`

command.

| apply <model name> [specialdays=<field name>] [target=<fields>] [holdback=<int | time-range>] [forecast_k=<int>] [conf_interval=<float>]

You can inspect the model learned by StateSpaceForecast with the `summary`

command.

| summary <model name>

**Syntax constraints**

- For univariate analysis the
`fields`

parameter is a single field, but for multivariate analysis it is a list of fields. - For multivariate analysis, only one
`specialdays`

field can be specified and it applies to all the fields. - The
`specialdays`

field values must be numeric. - Null values in the
`specialdays`

field are treated as 0. - Double quotes are required around field lists.
- Scoring metric values are based on all data and not on the
`holdback`

period data.

**Examples**

The following is a univariate example of StateSpaceForecast on a test set. The example is considered univariate as there is only a single field following `| fit StateSpaceForecast`

.

| inputlookup milk2.csv | fit StateSpaceForecast milk_production from * specialdays=holiday into milk_model | apply milk_model specialdays=holiday forecast_k=30

The following is a multivariate example of StateSpaceForecast on a test set. The syntax is the same as that in the univariate example, except that this case has a list of fields (CRM, ERP, and Expenses) following `| fit StateSpaceForecast`

, making it multivariate.

| inputlookup app_usage.csv | fields CRM ERP Expenses | fit StateSpaceForecast CRM ERP Expenses holdback=12 into app_usage_model as "crm, erp"

The following example is also multivariate and includes the `target`

field. In this example the fields of `CRM`

and `ERP`

are forecast using historical data and the `Expenses`

field. The `apply`

command is used against the model created in the `fit`

command step, resulting in the ` app_usage_model`

model.

Double quotes are required around any field list.

| inputlookup app_usage.csv | fields CRM ERP Expenses | apply app_usage_model target="CRM, ERP" forecast_k=36 holdback=36

The following example is again multivariate but without the `target`

field. This example forecasts the fields `CRM`

, `ERP`

, and `Expenses `

using historical data.

| inputlookup app_usage.csv | fields CRM ERP Expenses | apply app_usage_model forecast_k=36 holdback=36

The following example shows how to improve your output with StateSpaceForecast.

| inputlookup cyclical_business_process_with_external_anomalies.csv | eval holiday=if(random()%100<98,0,1) | fit StateSpaceForecast logons from logons into My_Model forecast_k=3000

Adding of the SPL line `period=2016`

could improve the output, but would not account for the period being seven days rather than twenty-four hours.

| inputlookup cyclical_business_process_with_external_anomalies.csv | table _time,logons | eval holiday=if(random()%100<98,0,1) | eval dayOfWeek=strftime(_time,"%a") | eval holidayWeekend=case(in(dayOfWeek,"Sat","Sun"),1,true(),0) | apply MyBadModel specialdays=holidayWeekend forecast_k=3000 | eval old_predict='predicted(logons)' | eval dayOfWeek=strftime(_time,"%a") | eval holidayWeekend=case(in(dayOfWeek,"Sat","Sun"),1,true(),0) | apply My_Model specialdays=holidayWeekend holdback=3000 forecast_k=3000

## Utility Algorithms

These utility algorithms are not machine learning algorithms, but provide methods to calculate data characteristics. These algorithms facilitate the process of algorithm selection and parameter selection. See the StatsModels documentation at http://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.acf.html for more information.

### ACF (autocorrelation function)

ACF (autocorrelation function) calculates the correlation between a sequence and a shifted copy of itself, as a function of `shift`

. Shift is also referred to as lag or delay.

**Parameters**

- The
`k`

parameter specifies the number of lags to return autocorrelation for. By default`k`

is 40. - The
`fft`

parameter specifies whether ACF is computed via Fast Fourier Transform (FFT). By default`fft`

is False. - The
`conf_interval`

parameter specifies the confidence interval in percentage to return. By default`conf_interval`

is set to 95.

**Syntax**

fit ACF <field> [k=<int>] [fft=true|false] [conf_interval=<int>]

**Example**

The following example uses ACF (autocorrelation function) on a test set.

... | fit ACF logins k=50 fft=true conf_interval=90

### PACF (partial autocorrelation function)

PACF (partial autocorrelation function) gives the partial correlation between a sequence and its lagged values, controlling for the values of lags that are shorter than its own. See the StatsModels documentation at http://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.pacf.html for more information.

**Parameters**

- The
`k`

parameter specifies the number of lags to return partial autocorrelation for. By default`k`

is 40. - The
`method`

parameter specifies which method for the calculation to use. By default`method`

is unbiased. - The
`conf_interval`

parameter specifies the confidence interval in percentage to return. By default`conf_interval`

is set to 95.

**Syntax**

fit PACF <field> [k=<int>] [method=<ywunbiased|ywmle|ols>] [conf_interval=<int>]

**Example**

The following example uses PACF (partial autocorrelation function) on a test set.

... | fit PACF logins k=20 conf_interval=90

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This documentation applies to the following versions of Splunk^{®} Machine Learning Toolkit:
4.2.0

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