Scoring metrics in the Machine Learning Toolkit
In the Splunk Machine Learning Toolkit (MLTK), the score
command runs statistical tests to validate model outcomes. You can use the score
command for robust model validation and statistical tests in any use case.
The score
command is only available on versions 4.0.0 or above of the MLTK. You need version 1.3 of the Python for Scientific Computing addon for version 4.0 or above of the MLTK. For more on version dependencies, see Upgrading the MLTK.
The MLTK uses the following classes of the score
command, each with their own sets of methods:
 Classification
 Clustering
 Pairwise distances scoring
 Regression scoring
 Statistical functions (statsfunctions)
 Statistical testing (statstest)
The Splunk Machine Learning Toolkit also enables the examination of how well your model might generalize on unseen data by using folds of the training set. This method is known as kfold scoring. The kfold
command does not use the score
command, but operates as a type of scoring.
Score commands cannot be customized within the Splunk Machine Learning Toolkit.
Classification
You can use classification scoring metrics to evaluate the predictive power of a classification learning algorithm.
Classification scoring in the Splunk Machine Learning Toolkit includes the following methods:
 Accuracy
 Confusion matrix
 F1score
 Precision
 PrecisionRecallF1Support
 Recall
 ROCAUCscore
 ROCcurve
Overview
The most common use of classification scoring is to evaluate how well a classification model performs on the test set. The inputs to the classification scoring methods are actual and predicted fields, corresponding to groundtruthlabels and predictedlabels, respectively. The syntax also supports the comparison of multiple fields, allowing for multifield comparisons. This is useful for evaluating which classification model is best suited for your data.
Classification scoring methods only work on categorical data such as integers and stringtypes, but not on floats. These methods are used to evaluate the output of classification algorithms, such as logistic regression. You may see an error message if you attempt to use the comparison scoring method on numeric floattype data.
Preprocessing
All classification scoring methods follow the same preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 You may receive an error if any categorical fields are found.
Parameters
 The
pos_label
parameter must be an element of all actual or ground truth data.  An error will display if the a valid value for
pos_label
is not found in an actual field.  Use the
pos_label
parameter to specify the positive class whenaverage=binary
.  The
pos_label
parameter is ignored if the average is not binary.  The
average
parameter includes several options including None, Binary, Micro, Macro and Weighted.
Parameter option for average

Use cases 

None  Returns the scoring metric for each unique class in the union of actual_field + predicted_field .

Binary  Reports results for the class specified by the pos_label parameter. This parameter only works for binary data and will display an error if applied to a multiclass problem.

Micro  Calculates metrics globally by counting the total truepositives, falsenegatives, and falsepositives. 
Macro  Calculates metrics for each label and finds their unweighted mean. Does not take label imbalance into account. 
Weighted  Calculates metrics for each label and finds their average weighted by support as in the number of true instances for each label. This alters the Macro to account for label imbalance and can result in an Fscore that is not between Precision and Recall. 
Syntax
As with all scoring methods, classification methods support pairwise comparisons between two sets of fields or arrays. The general syntax is as follows:
..  score <scoringmethodname> array_a against array_b [options]
 The
against
parameter separates the groundtruth fields (on the left) from the predicted fields."~"
is equivalent. 
array_a
represents the groundtruth fields, and is specified by fieldsactual_field_1 ... actual_field_n

array_b
represents the predicted fields, and is specified by fieldspredicted_field_1 ... bpredicted_field_n
SPL syntax
..  score <scoringmethodname> <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> [options]
Syntax constraints
Classification scoring supports the wildcard (*) character in cases of 1ton only.
Examples
The following example shows the loaded data split into training (<=70 partitions) and testing (>70 partitions) sets. Classification scoring is used, and the model saved as a knowledge object.
The training set is selected, and the model is applied to get predictions on unseen data, perform scoring, and analysis of the results.
The following syntax example is training multiple models on the same field.
 inputlookup iris.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression species from * into LR_model  fit RandomForestClassifier species from * into RF_model  fit GaussianNB species from * into GNB_model  fit DecisionTreeClassifier species from * into DT_model
The following syntax example is evaluating the ground truth field against multiple predictions.
 inputlookup iris.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_species  apply RF_model as RF_species  apply GNB_model as GNB_species  apply DT_model as DT_species  score precision_recall_fscore_support species ~ LR_species RF_species GNB_species DT_species average=weighted
The following visualization shows the evaluation of the ground truth field against multiple predictions.
Accuracy scoring
You can use accuracy scoring to get the prediction accuracy between actuallabels
and predictedlabels
.
Accuracy scoring implements sklearn.metrics.accuracy_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
Further reading: https://en.wikipedia.org/wiki/Accuracy_and_precision
Parameters
 The
normalize
parameter default is True.  The
normalize
parameter dictates whether to return the raw count of correctly classified samples (normalize=False) or the fraction of correctly classified samples (normalize=True).  When the
pos_label
parameteraverage=binary
and the combined cardinality of the actual or predicted field is <= 2, the report results forclass=pos_label
only.
Syntax
...score accuracy_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> normalize=<TrueFalse>
Syntax constraints
 Accuracy scoring supports 1to1, nton, and 1ton comparison syntaxes.
 Accuracy scoring supports the wildcard (*) character in cases of 1ton only.
Example
You manually specify fields because a predicted field exists in the data. In particular, manually specify fields for second call to the fit
command and onwards.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into LR_model  fit DecisionTreeClassifier vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into DT_model
After training a classifier to predict vehicle type, you can analyze your test set accuracy.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_prediction  apply DT_model as DT_prediction  score accuracy_score vehicleType against LR_prediction DT_prediction
Example output
Confusion matrix
You can use a confusion matrix to get the prediction accuracy between actuallabels
and predictedlabels
.
Implements sklearn.metrics.confusion_matrix
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
Further reading: https://en.wikipedia.org/wiki/Confusion_matrix
Parameters
The confusion matrix takes no parameters.
Syntax
score confusion_matrix <actual_field> against <predicted_field>
Syntax constraints
 The
groundtruthlabels
map along the vertical event axis, and thepredictedlabels
map along the horizontal fieldaxis.  Works only for 11 comparisons, because the output of
confusion_matrix
is already 2d.  Confusion matrix scoring does not support the wildcard (*) character.
Although order is not preserved in the output fields and events, the correspondence of fields and events is preserved.
Example
The following example uses a confusion matrix to test actual vehicle type against predicted vehicle type.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply DT_model as DT_prediction  score confusion_matrix vehicleType against DT_prediction
Example output
The following visualization of the confusion matrix shows which classes were most and least successfully predicted, as well as what they were mistaken for.
F1score
You can use the F1score to get the prediction accuracy between truelabels
and predictedlabels
.
Implements sklearn.metrics.f1_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.f1_score.html
Further reading: https://en.wikipedia.org/wiki/F1_score
Parameters
 The
pos_label
parameter default is 1.  When the
pos_label
parameteraverage=binary
and the combined cardinality of the actual or predicted field is <= 2, the report results forclass=pos_label
only.  The
average
parameter default is binary.
Syntax
score f1_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> average=<binary(default)  micro  macro  weighted> pos_label=<str  int>
Syntax constraints
 F1score supports 1to1, nton, and 1ton comparison syntaxes.
 F1score supports the wildcard (*) character in cases of 1ton only.
Example
The following example tests the prediction of vehicle type using F1score.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into LR_model  fit DecisionTreeClassifier vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into DT_model
After training a classifier to predict vehicle type, you can evaluate your model's precision on the training set for each vehicle type.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_prediction  apply DT_model as DT_prediction  score f1_score vehicleType against LR_prediction DT_prediction average=micro
Example output
The following visualization shows the F1score model on a test set for each vehicle type with LogisticRegression results on the left and DecisionTree results on the right. The visualization also shows the average across all vehicle types.
Precision
You can use precision scoring to get the prediction accuracy between actuallabels
and predictedlabels
.
Implements sklearn.metrics.precision_score
. Learn more here:http://scikitlearn.org/stable/modules/generated/sklearn.metrics.precision_score.html
Further reading: https://en.wikipedia.org/wiki/Accuracy_and_precision
Parameters
 The
pos_label
parameter default is 1.  When the
pos_label
parameteraverage=binary
and the combined cardinality of the actual or predicted field is <= 2, the report results forclass=pos_label
only.  The
average
parameter default is binary.
Syntax
...score precision_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> average=<binary(default)micromacroweighted> pos_label=<strint>
Syntax constraints
 Precision scoring supports 1to1, nton and 1ton. comparison syntaxes.
 Precision scoring supports the wildcard (*) character in cases of 1ton only.
Example
The following example tests the prediction of vehicle type using precision scoring.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into LR_model  fit DecisionTreeClassifier vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into DT_model
After training a classifier to predict vehicle type, you can evaluate the model's precision on the training set for each vehicle type.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_prediction  apply DT_model as DT_prediction  score precision_score vehicleType against LR_prediction DT_prediction average=None
Example output
The following visualization shows the precision model on a test set for each vehicle type with LogisticRegression results on the left and DecisionTree results on the right. A warning shows that rows containing NAN values and have been removed.
PrecisionRecallF1Support
You can use PrecisionRecallF1Support scoring to get the precision, recall, F1score and support prediction accuracy between actualfields
and predictedfields
.
Implements sklearn.metrics.precision_recall_fscore_support
. Learn more here :http://scikitlearn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
Parameters
 The
pos_label
parameter default is 1.  When the
pos_label
parameteraverage=binary
and the combined cardinality of the actual or predicted field is <= 2, the report results forclass=pos_label
only.  The
average
parameter default is None.  The
beta
parameter default is 1.0.  The
beta
parameter shows the strength of recall versus precision infscore
.
Syntax
score precision_recall_fscore_support <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> pos_label=<str> average=<str> beta=<float>
Syntax constraints
You can refer to the following table to distinguish your results when average=None and when average=Not None.
Average  Result 

Not None  Works for all syntax constraints including 1to1 and 1ton. 
None  Only works for 11 comparisons because the output of precision_recall_fscore_support is already 2d. Support scoring is only defined when average=None because averaged values are not generated for support.

PrecisionRecallF1Support supports the wildcard (*) character in cases of 1ton only.
Example
The following example tests the prediction of vehicle type using PrecisionRecallF1Support scoring.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into LR_model  fit DecisionTreeClassifier vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into DT_model
After training a classifier to predict vehicle type, you can evaluate your model's precision on the training set for each vehicle type.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_prediction  apply DT_model as DT_prediction  score precision_recall_fscore_support vehicleType against LR_prediction DT_prediction average=weighted
Example output
The following visualization shows the precision, recall, and f_beta scores for the prediction of vehicle type, under a weighted averaging scheme.
Recall
You can use recall scoring to get the prediction accuracy between actuallabels
and predictedlabels
.
Implements sklearn.metrics.recall_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.recall_score.html
Further reading: https://en.wikipedia.org/wiki/Precision_and_recall
Parameters
 The
pos_label
parameter default is 1.  When the
pos_label
parameteraverage=binary
and the combined cardinality of the actual or predicted field is <= 2, the report results forclass=pos_label
only.  The
average
parameter default is binary.
Syntax
score recall <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> average=<binary(default)  micro  macro  weighted> pos_label=<str  int>
Syntax constraints
 Recall supports 1to1, nton, and 1ton comparison syntaxes.
 Recall partially supports the wildcard (*) character in cases of 1ton only.
Example
The following example tests the prediction of vehicle type using recall scoring.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into LR_model  fit DecisionTreeClassifier vehicleType from batteryVoltage engineCoolantTemperature engineSpeed into DT_model
After training a classifier to predict vehicle type you can evaluate your model's precision on the training set for each vehicle type.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model as LR_prediction  apply DT_model as DT_prediction  score recall_score vehicleType against LR_prediction DT_prediction average=weighted
Example output
The following visualization shows the recall model on a test set with LogisticRegression results on the left and DecisionTree results on the right. The visualization shows the average across all vehicle types where the average is weighted by the support.
ROCAUCscore
You can use ROCAUC scoring to get the prediction accuracy between actuallabels
and predictedscores
.
Implements sklearn.metrics.roc_auc_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html
Further reading: https://en.wikipedia.org/wiki/Receiver_operating_characteristic
Parameters
 Although
sklearn.metrics.roc_auc_score
supports anaverage
parameter, this parameter is currently disabled as the toolkit does not support thelabelindicator
format.  The
pos_label
parameter default is 1.  You can use the
pos_label
label when data is multi class but you want to apply binary scoring methods. Thepos_label
label allows multiclass data to cast to binary by specifying the class identified aspos_label
as the positive class, and all other classes as negative. For the original multiclass data ofa, b, c, a, e
whenpos_label=a
the resulting binary data is1, 0, 0, 1, 0
.  When the predicted field contains target scores, that field can either be probability estimates of the positive class, confidence values, or a nonthresholded measure of decisions.
Requirements
 ROCAUCscore only applies to binary data. To support multiclass problems, binarize the data using the
pos_label
parameter.  The predicted field must be numeric. The numeric data must be float or integer type, corresponding to probability estimates of the positive class, confidence values, or a nonthresholded measure of decisions as returned by the
decision_function
parameter on some classifiers.  If the predicted field does not meet the numeric criteria, an error message will display.
If  Then 

Binary data is given  The data must be true binary such as {0,1} or {1,1}. 
Binary is not data such as multiclass  The pos_label parameter must be specified and contained in the ground_truth field.

Binary is not true binary  The pos_label parameter must be specified and contained in the ground_truth field.

If the pos_label
parameter is not in the ground_truth
field, an error message will display.
If the ground truth data is multiclass and the pos_label
parameter is properly specified, you may see an error message.
Syntax
score roc_auc_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> pos_label=<str  int>
Syntax constraints
 ROCAUCscore supports 1to1, nton, and 1ton comparison syntaxes.
 ROCAUCscore does not support the wildcard (*) character.
Example
The following example shows how you can obtain the area under the ROC curve for predicting the vehicle type of 2013 Audi RS5.
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression vehicleType from * probabilities=True into LR_model
 inputlookup track_day.csv  sample partitions=100 seed=1234  search partition_number > 70  score roc_auc_score vehicleType against "probability(vehicleType=2013 Audi RS5)" pos_label="2013 Audi RS5"
Example output
The following visualization shows the results of the ROCAUC scoring on a test set.
ROCcurve
You can use ROCcurve scoring to get the prediction accuracy between actualfields
and predictedfields
.
Implements sklearn.metrics.roc_curve
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
Further reading: https://en.wikipedia.org/wiki/Receiver_operating_characteristic
Parameters
 The
pos_label
parameter default is 1.  You can use the
pos_label
label when data is multiclass but you want to apply binary scoring methods. Thepos_label
label allows multiclass data to cast to binary by specifying the class identified aspos_label
as the positive class, and all other classes as negative. The original multiclass data ofa, b, c, a, e
whenpos_label=a
results in the binary data of1, 0, 0, 1, 0
.  The
drop_intermediate
parameter default is True. Whether to drop sub optimal thresholds which would not appear on a plotted ROCcurve. This is useful when creating lighter ROC curves.
Requirements
 ROCcurve only applies to binary data. To support multiclass problems, convert the data into binary with the
pos_label
parameter.  The predicted field must be numeric. The numeric data must be float or integer type, corresponding to probability estimates of the positive class, confidence values, or nonthresholded measure of decisions.
 If the predicted field does not meet the numeric criteria, an error message will display.
If  Then 

Binary data is given  It must be truebinary such as {0,1} or {1,1}. 
Binary is not data such as multiclass  The pos_label parameter must be specified and contained in the ground_truth field.

Binary is not true binary  The pos_label parameter must be specified and contained in the ground_truth field.

If the pos_label
parameter is not in the ground_truth
field, an error message displays.
If the ground truth data is multiclass and the pos_label
is properly specified, you are warned of the conversion.
Syntax
score roc_curve <actual_field> against <predicted_field> pos_label=<strint> drop_intermediate=<TrueFalse>
Syntax constraints
 ROCcurve scoring only works for 11 comparisons.
 ROCcurve scoring does not support the wildcard (*) character.
Example
The following example tests the probability of churn using ROCcurve scoring.
 inputlookup churn.csv  sample partitions=100 seed=1234  search partition_number <= 70  fit LogisticRegression Churn? from * probabilities=True into LR_model
 inputlookup churn.csv  sample partitions=100 seed=1234  search partition_number > 70  apply LR_model probabilities=True  score roc_curve Churn? against "probability(Churn?=True.)" pos_label='True.'
Example output
The following visualization shows how the true positive rate (tpr) varies with the false positive rate (fpr), along with the corresponding probability thresholds.
Clustering scoring
You can use clustering scoring to evaluate the predicted value of a clustering model. The inputs to the clustering scoring methods are arrays of data specified by an ordered sequence of fields.
Clustering scoring in the Splunk Machine Learning Toolkit includes the following methods:
Overview
Clustering scoring methods can operate on two arrays. The label
and features
fields are specified by the ordered sequence of the fields <label_field>
and feature_field_1 feature_field_2 ... feature_field_n
respectively.
You can use the against
clause to separate the arrays where label_field against feature_field_1 feature_field_2 ... feature_field_n
correspond to label
(ground truth or predicted labels) against features (features used by clustering algorithm), respectively.
Clustering scoring methods will only work on numerical data, and are expected to be used to evaluate the output of clustering models such as KMeans and Spectral Clustering. Attempting to score on categorical data will display an error.
Neither parameters that take a list or array as input or metrics that calculate the distance between categorical arrays are supported.
Preprocessing
Clustering scoring methods perform the following preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 The
label
field is converted to categorical.  An error message displays if any categorical fields are found in the feature fields.
Parameters
 The
metric
parameter default is euclidean.  Supported
metric
values include: cityblock, cosine, euclidean, l1, l2, manhattan, braycurtus, canberra, chebyshev, correlation, hamming, matching, minkowski, and sqeuclidean.  The wildcard (*) character is disabled for clustering scoring methods.
 The number of fields in the
label_array
parameter is limited to one and it must be categorical.
Silhouette score
You can use the silhouette score to calculate the prediction accuracy between label_array
and feature_array
.
Implements sklearn.metrics.silhouette_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
Further reading: https://en.wikipedia.org/wiki/Silhouette_(clustering)
Parameters
Silhouette score supports clustering scoring parameters.
Syntax
...score silhouette_score <label_field> against <feature_field_1> ... <feature_field_n> metric=<euclidean(default)  cityblock  cosine  l1  l2  manhattan  braycurtis  canberra  chebyshev  correlation  hamming  matching  minkowski  sqeuclidean>
Syntax constraints
Silhouette score supports the following syntax constraints:

label_field
parameter must have only single field. 
feature_fields
parameter can have single or multiple fields.  Silhouette score supports the wildcard (*) character in cases of 1ton only.
Example
The following example uses silhouette scoring to calculate species prediction on a test data set.
 inputlookup iris.csv  fit StandardScaler petal_length petal_width sepal_length sepal_width as scaled  fit KMeans scaled_* k=3 as KMeans_predicted_species  score silhouette_score KMeans_predicted_species against scaled_petal_length scaled_petal_width scaled_sepal_length scaled_sepal_width
Example output
The following visualization shows the results of silhouette scoring on the iris dataset.
Pairwise distances scoring
You can use pairwise distances scoring to calculate the distances between different fields.
Pairwise distances scoring in the Splunk Machine Learning Toolkit includes the following methods:
Overview
The inputs to the pairwise distances scoring methods are array(s) of data specified by an ordered sequence of fields. The arrays for pairwise distances scoring methods are a_array
and b_array
.
Pairwise distances scoring methods support pairwise comparisons between two sets of fields or arrays such as a_field_1 a_field_2 ... a_field_n
and b_field_1, b_field_2, b_field_m
respectively. The general syntax is as follows:
.. score <scoringmethodname> a_field_1 a_field_2 ... a_field_n against b_field_1 b_field_2 ... b_field_m [options]
The against
clause separates arrays. The "~"
symbol is equivalent.
In general, statistical methods are commutative such that a_field against b_field
is equivalent to b_field against a_field
. The arrays a_array
and b_array
are specified by a sequence of fields: a_field_1 ... a_field_n
and b_field_1 ... b_field_m
.
Pairwise distances scoring methods only work on numerical data. Attempting to score on categorical data will display an error.
Preprocessing
All pairwise distance scoring methods follow the same preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 An error message displays if any categorical fields are found in both arrays.
Parameters
 The
metric
parameter default = euclidean.  Supported
metric
values include: cityblock, cosine, euclidean, l1, l2, manhattan, braycurtis, canberra, chebyshev, correlation, hamming, matching, minkowski, sqeuclidean, KolmogorovSmirnov (2 samples), and Wasserstein distance.  The
output
parameter default = matrix.  Pairwise distances scoring supports the wildcard (*) character.
 Supported output values are matrix and list.
Using metric values of KolmogorovSmirnov (2 samples) or Wasserstein distance requires running version 1.4 of the Python for Scientific Computing addon.
Some parameters have not been supported:
 Parameters that take a list or array as input.
 Metrics that calculate the distance between categorical arrays.
Pairwise distances score
Calculate the pairwise distances score between a_array
and b_array
.
Implements sklearn.metrics.pairwise.pairwise_distances
. Learn more here: http://scikitlearn.org/0.19/modules/generated/sklearn.metrics.pairwise.pairwise_distances.html
Parameters
Pairwise distances score score supports pairwise distances scoring parameters.
 The metric parameter default =
euclidean
.  Supported metric values include: cityblock, cosine, euclidean, l1, l2, manhattan, braycurtis, canberra, chebyshev, correlation, hamming, matching, minkowski, sqeuclidean KolmogorovSmirnov (2 samples), and Wasserstein distance.
 The output parameter default =
matrix
.  Supported output values are matrix and list.
 In cases of eventvsevent distances Pairwise distances support only fieldvsfield (columnwise) distances. If you need to calculate the distances between events you can transpose the matrix first and then use the
pairwise_distances
score command.  Pairwise distances scoring supports the wildcard (*) character.
Using metric values of KolmogorovSmirnov (2 samples) or Wasserstein distance requires running version 1.4 of the Python for Scientific Computing addon.
Syntax
...score pairwise_distances <a_field_1> ... <a_field_n> against <b_field_1> ... <b_field_m> metric= <euclidean(default)  cityblock  cosine  l1  l2  manhattan  braycurtis  canberra  chebyshev  correlation  hamming  matching  minkowski  sqeuclidean  ks_2samp  wasserstein_distance> output=<matrix(default)  list>
Syntax constraints
a_field
can have single or multiple fields with numbers that may be equal to each other, or differ from each other.b_field
can have single or multiple fields with numbers that may be equal to each other, or differ from each other.
Examples
The following example uses pairwise distances scoring on a test set.
 inputlookup iris.csv  score pairwise_distances petal_length petal_width AGAINST sepal_length sepal_width
The following visualization shows pairwise distance scoring on a test set.
The following example uses the output=list
parameter.
 inputlookup iris.csv  score pairwise_distances petal_length petal_width AGAINST sepal_length sepal_width output=list
The following visualization shows pairwise distance scoring on a test set including the output=list
parameter.
The following example uses eventvsevent distances on a test set.
 inputlookup iris.csv  table petal* sepal*  transpose 0
The following visualization shows eventvsevent distances on a test set.
The following example uses the output=list
parameter on a test set. .
 inputlookup iris.csv  table petal* sepal*  transpose 0  score pairwise_distances "row 1" "row 2" AGAINST "row 3" "row 4" output=list  fields *_fields pairwise*
The following visualization shows the output=list
parameter on a test set.
Regression scoring
Use regression scoring metrics to evaluate the predictive power of a regression learning algorithm. The most common use of regression scoring is to evaluate how well a regression model performs on the test set.
Regression scoring in the Splunk Machine Learning Toolkit includes the following methods:
The inputs to the regression scoring methods are arrays of data specified by an ordered sequence of fields.
Regression scoring methods can operate on two arrays. The actual
and predicted
fields are specified by an ordered sequence of fields actual_field_1 .. actual_field_n
and predicted_field_1 ... predicted_field_n
, respectively.
You can use the against
clause to separate the arrays where actual_field_1 ... actual_field_n
against predicted_field_1 ... predicted_field_n
correspond to to actual (ground truth target values) against predicted (predicted target values), respectively.
These scoring methods only work on numerical data, and are used to evaluate the output of regression algorithms such as Gradient Boosting Regression and Linear Regression.
Attempting to score on categorical data, or having no numerical fields in any of the arrays displays an error.
Preprocessing
Regression scoring methods follow the same preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 An error message displays if any categorical fields are found in the feature fields.
Parameters
 The
multioutput
parameter default israw_values
.  The
raw_values
parameter returns a full set of regressions scores or errors between each field infields_a
andfield_b
respectively.  The wildcard (*) character is supported in cases of 1ton only.
 The number of fields in
actual_fields
andpredtcted_fields
must be either equal to each other or one of them must have only one field.  If one of the arrays has a single field and the other array has multiple fields, the
multioutput
parameter is set toraw_values
.  If one of the arrays has a single field and the other array has multiple fields, the regression score is calculated between each field of the array which has multiple fields and the one field of the array that has a single field.
 If the
multioutput
parameter was set to a different value by the user beforehand, an error displays.
Parameters that take a list or array as input are not supported.
Explained variance score
You can use explained variance score to calculate the explained variance regression score between predicted and actual fields.
Implements sklearn.metrics.explained_variance_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score
Further reading: https://en.wikipedia.org/wiki/Explained_variation
Parameters:
 The
multioutput
parameter default israw_values
.  The
variance_weighted
parameter is the scores of all outputs averages with the weights of each individual output's variance.  To see each explained variance score compared to the actual score, set the multioutput parameter to
raw_values
.
Syntax
...score explained_variance_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> multioutput=<raw_values(default)  uniform_average  variance_weighted>
Syntax constraints
 Explained variance score supports 1to1, nton, and 1ton comparison syntaxes.
 Explained variance score supports the wildcard (*) character in 1ton cases.
Explained variance score is not symmetric.
Example
The following example shows manually specified fields in particular for the second call to the fit
command and onwards because a predicted field exists in the data.
 inputlookup server_power.csv  fit LinearRegression ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization as ac_power_LR  fit RandomForestRegressor ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization as ac_power_RFR  score explained_variance_score ac_power_LR against ac_power_RFR
To see each explained variance score compared to the actual score, set the multioutput
parameter to raw_values
.
Example output
The following visualization shows the results of explained variance score on a test set.
Mean absolute error score
You can use mean absolute error scoring to calculate regression loss between actual_fields
and predicted_fields
.
Implementssklearn.metrics.mean_absolute_error
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error
Further reading: https://en.wikipedia.org/wiki/Mean_absolute_error
Parameters
Mean absolute error score supports regression scoring parameters.
Syntax
...score mean_absolute_error <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> multioutput=<raw_values(default)  uniform_average>
Syntax constraints
 Mean absolute error score supports 1to1, nton, and 1ton comparison syntaxes.
 Mean absolute error score supports the wildcard (*) character in 1ton cases.
Example
The following example shows manually specified fields particularly for the second call to the fit
command and onwards becausea predicted field exists in the data.
To see each mean absolute score compared to the actual score the multioutput
parameter must be set to raw_values
. If set to another value a warning message displays.
 inputlookup power_plant.csv  fit LinearRegression Energy_Output from Temperature Pressure Humidity Vacuum fit_intercept=true as energy_output_LR  fit Lasso Energy_Output from Temperature Pressure Humidity Vacuum as energy_output_LASSO  score mean_absolute_error Energy_Output against energy_output_LR energy_output_LASSO multioutput=uniform_average
Example output
The following visualization shows mean absolute error scoring on a test set.
Mean squared error
You can use mean squared error score to calculate regression loss between actual_fields
and predicted_fields
.
Implements sklearn.metrics.mean_squared_error
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error
Further reading: https://en.wikipedia.org/wiki/Mean_squared_error
Parameters
Mean squared error score supports regression scoring parameters.
Syntax
...score mean_squared_error <actual_field__1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> multioutput=<raw_values(default)  uniform_average>
Syntax constraints
 Mean squared error score supports 1to1, nton, and 1ton comparison syntaxes.
 Mean squared error score supports the wildcard (*) character in 1ton cases.
Example
The following example shows manually specified fields particularly for the second call to the fit
command and onwards because a predicted field exists in the data.
 inputlookup power_plant.csv  fit LinearRegression Energy_Output from Temperature Pressure Humidity Vacuum fit_intercept=true as energy_output_LR  fit Lasso Energy_Output from Temperature Pressure Humidity Vacuum as energy_output_LASSO  score mean_squared_error Energy_Output against energy_output_LR energy_output_LASSO multioutput=raw_values
Example output
The following visualization shows mean squared error scoring on a test set.
R2 score
You can use this method to calculate the R2 score between actual_fields
and predicted_fields
.
Implements sklearn.metrics.r2_score
. Learn more here: http://scikitlearn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score
Further reading: https://en.wikipedia.org/wiki/Coefficient_of_determination
Parameters
 The
multioutput
parameter default israw_values
.  The
variance_weighted
parameter is the scores of all outputs, averaged with weights of each individual output's variance.  The
none
parameter acts the same as theuniform_average
parameter.
Syntax
...score r2_score <actual_field_1> ... <actual_field_n> against <predicted_field_1> ... <predicted_field_n> multioutput=<raw_values(default)  uniform_average  variance_weighted  None >
Syntax constraints
 R2 score supports 1to1, nton, and 1ton comparison syntaxes.
 R2 score supports the wildcard (*) character in 1ton cases.
R2 score is not symmetric.
Example
The following example shows manually specified fields particularly for the second call to the fit
command and onwards because a predicted field exists in the data.
 inputlookup server_power.csv  fit LinearRegression ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization into LR_model  fit RandomForestRegressor ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization into RFR_model  fit Lasso ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization into LASSO_model  fit DecisionTreeRegressor ac_power from totalcpuutilization totaldiskaccesses totaldiskutilization into DTR_model
After training several regressors to predict ac_power
, you can analyze their predictions compared to the ground truth.
 inputlookup server_power.csv  apply LR_model as LR_prediction  apply RFR_model as RFR_prediction  apply LASSO_model as LASSO_prediction  apply DTR_model as DTR_prediction  score r2_score ac_power against LR_prediction RFR_prediction LASSO_prediction DTR_prediction
Example output
The following visualization shows R2 scoring on a test set.
Statistical functions (statsfunctions)
Statistical functions are general statistical methods that either provide statistical information about data or perform a statistical test on data. A statistic/pvalue is not returned.
Statistical functions scoring in the Splunk Machine Learning Toolkit include the following methods:
Preprocessing
All statistical functions scoring methods follow the same preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 An error message displays if any categorical fields are found in the feature fields.
Parameters
Statistical functions support the wildcard (*) character in single array cases only.
Describe
You can use Describe scoring to compute several descriptive statistics of the passed array.
Implements scipy.stats.describe
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.describe.html
Parameters
 The
ddof
parameter default is 1.  The
ddof
parameter stands for delta degrees of freedom.  The
ddof
parameter is only used for variance.  If the
bias
parameter is False, then the skewness and kurtosis calculations are corrected for statistical bias.
Syntax
score describe <a_field_1> <a_field_2> ... <a_field_n> ddof=<int> bias=<truefalse>
Syntax constraints
 Describe scoring supports the wildcard (*) character.
 A single sequence of fields.
Example
The following example uses Describe scoring on a test set.
 inputlookup diabetes.csv  score describe blood_pressure diabetes_pedigree glucose_concentration
Example output
The following visualization shows Describe scoring on a test set.
Moment
A Moment is a specific quantitative measure of the shape of a set of points. It is often used to calculate coefficients of skewness and kurtosis due to its close relationship with them
Implements scipy.stats.moment
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.moment.html
Further reading: https://en.wikipedia.org/wiki/Moment_(mathematics)
Parameters
The moment
parameter default is 1.
Syntax
score moment <a_field_1> <a_field_2> ... <a_field_n> moment=<int>
Syntax constraints
 Moment scoring supports the wildcard (*) character.
 A sequence of fields.
Example
The following example calculates the third Moment of the given data.
 inputlookup diabetes.csv  score moment blood_pressure diabetes_pedigree glucose_concentration moment=3
Example output
The following visualization shows Moment scoring on a test set.
Pearson
You can use pearson scoring to calculate a pearson correlation coefficient and the pvalue for testing noncorrelation.
Implements scipy.stats.spearmanr
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html
Further reading: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
Parameters
Pearson scoring has no parameters.
Syntax
score pearsonr <a_field> against <b_field>
Syntax constraints
 A pair of fields such as a 1to1 comparison.
 Pearson scoring does not support the wildcard (*) character.
Returns
Pearson scoring returns the correlation coefficient and the pvalue for testing noncorrelation.
Example
The following example uses Pearson scoring on a test set.
 inputlookup track_day.csv  score pearsonr engineSpeed against speed
Example output
The following visualization shows Pearson scoring on a test set.
Spearman
You can use Spearman scoring to calculate the rankorder correlation coefficient and the pvalue to test for noncorrelation.
Implements scipy.stats.spearmanr
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html
Further reading: https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
Parameters
Spearman scoring has no parameters.
Syntax
score spearmanr <a_field> against <b_field>
Syntax constraints
 A pair of fields such as a 1to1 comparison.
 Spearman scoring does not support the wildcard (*) character.
Returns
Spearman scoring returns the correlation coefficient and the pvalue to test for noncorrelation.
Example
The following example uses Spearman scoring on a test set.
 inputlookup track_day.csv  score spearmanr engineSpeed against speed
Example output
The following visualization shows Spearman scoring on a test set.
Tmean
The Tmean function finds the arithmetic mean of given values, and ignores values outside the given limits.
Implements scipy.stats.tmean
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.tmean.html
Further reading: https://en.wikipedia.org/wiki/Truncated_mean
Parameters
 The optional
lower_limit
parameter default is None.  The
lower_limit
parameter represents the lower bound of data to include. If None, there is no lower bound.  The optional
upper_limit
parameter default is None.  The
upper_limit
parameter represents the upper bound of data to include. If None, there is no upper bound.
Syntax
score tmean <a_field_1> ... <a_field_n> lower_limit=<floatNone> upper_limit=<floatNone>
A global trimmed mean is calculated across all fields.
Syntax constraints
 Tmean supports the wildcard (*) character.
 A sequence of fields.
Returns
The Tmean function returns a single value representing the trimmed mean of the data as in the mean ignoring samples outside of the given bounds.
Example
The following example shows the Tmean function on a test set.
 inputlookup diabetes.csv  score tmean blood_pressure diabetes_pedigree glucose_concentration lower_limit=1 upper_limit=1
Example output
The following visualization shows the trimmed mean result for the test set.
Trim
The Trim function slices off a proportion of items from both ends of an array.
Implements scipy.stats.trim
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.trimboth.html
Parameters
 The
tail
parameter default is both.  The
tail
parameter determines whether to cut off data from the left, right or both sides of the distribution.  In the
proportiontocut
parameter you must specifyfloat
.
Syntax
score trim <a_field_1> ... <a_field_n> proportiontocut=<float> tail=<leftrightboth>
Syntax constraints
 Trim supports the wildcard (*) character.
 A sequence of fields.
Returns
The Trim function returns a shortened version of the data where the order of the trimmed content is undefined.
Example
The following example uses the Trim function on a test set.
 inputlookup diabetes.csv  score trim glucose_concentration tail=both proportiontocut=0.1
Example output
The following visualization shows the Trim function on a test set.
Tvar
You can use the Tvar function to compute the sample variance of an array of values, while ignoring values that are outside of given limits.
Implements scipy.stats.ttest_ind
. Learn more here: https://docs.scipy.org/doc/scipy0.14.0/reference/generated/scipy.stats.tvar.html
Parameters
 The optional
lower_limit
parameter default is None.  The
lower_limit
parameter represents the lower bound of data to include. If None, there is no lower bound.  The optional
upper_limit
parameter default is None.  The
upper_limit
parameter represents the upper bound of data to include. If None, there is no upper bound.
Syntax
score tvar <a1> <a2> ... <an> lower_limit=<floatNone> upper_limit=<floatNone>
A global trimmed variance is calculated across all fields.
Syntax constraints
 Tvar supports the wildcard (*) character.
 A sequence of fields.
Returns
The Tvar function returns a single value representing the trimmed variance of the data such as the variance while ignoring samples outside of the given bounds.
Example
The following example uses the Tvar function on a test set.
 inputlookup diabetes.csv  score tvar blood_pressure diabetes_pedigree glucose_concentration lower_limit=1 upper_limit=1
Example output
The following visualization shows the Tvar function on a test set.
Statistical testing (statstest)
Statistical testing (statstest) scoring is used to validate or invalidate a statistical hypothesis. The output of statstest scoring methods is a testspecific statistic and a corresponding pvalue.
All statisticaltesting methods support the parameter alpha
, which indicates the alphalevel or significantlevel for the statistical test. The default value is 0.05.
Statistical testing in the Splunk Machine Learning Toolkit includes the following methods:
 Analysis of Variance (Anova)
 Augmented DickneyFuller (Adfuller)
 KolmogorovSmirnov (KS) test (1 sample)
 KolmogorovSmirnov (KS) test (2 samples)
 KwiatkowskiPhillipsSchmidtShin (KPSS)
 Mannwhitneyu
 Normal test
 Oneway ANOVA
 Ttest (1 sample)
 Ttest (2 independent samples)
 Ttest (2 related samples)
 Wasserstein distance
 Wilcoxon
Overview
The inputs to the statstest scoring methods are arrays of data specified by an ordered sequence of fields. The arrays for statistical testing methods are referred to as a_array
and b_array
.
In general, statistical testing methods are commutative as in a_field against b_field
being equivalent to b_field against a_field
.
Arrays array_a
and array_b
are specified by a sequence of fields: a_field_1 ... a_field_n
and b_field_1 ... b_field_n
.
Statstest scoring methods can operate on a single array or two arrays.
Array count  Example syntax 

One  ... score describe <array_a> 
Two  ... score ks_2samp <array_a> against <array_b> 
Preprocessing
All statistical testing scoring methods follow the same preprocessing steps:
 Search commands are pulled into memory.
 The data is prepared:
 All rows containing NAN values are removed prior to computing the score.
 An error message displays if any categorical fields are found in the feature fields.
For scoring methods requiring 2 arrays, use the against
clause to separate the arrays. You can use "~"
as an equivalent to against
.
Analysis of Variance (Anova)
Computes the Analysis of Variance (Anova) table for a fitted ordinary linear regression (OLS) model on the fields provided in the formula.
Implements statsmodels.stats.anova.anova_lm. Learn more here: https://www.statsmodels.org/stable/generated/statsmodels.stats.anova.anova_lm.html#statsmodels.stats.anova.anova_lm
Further reading: https://en.wikipedia.org/wiki/Analysis_of_variance
Parameters
 The
type
parameter indicates the type of Anova test to perform and can take the values of 1, 2, and 3.  The
type
parameter default is 1.  The
scale
parameter indicates variance estimation. Variance is estimated from the largest model if value for scale is None.  The
scale
parameter default is None.  Use the
test
parameter to test which statistics to provide and can take the values off
,chisq
, andcp
.  The
test
parameter default isf
.  Use the
robust
parameter for covariance type.  The
robust
parameter can take the values ofhc0
,hc1
,hc2
,hc3
, andNone
.hc
represents heteroscedasticitycorrected coefficient covariance matrix. For robust covariance
hc3
is recommended.
 Use the
output
parameter for tables to present.  The
output
parameter can take the values ofanova
,model_accuracy
, andcoefficients
.  The
output
parameter default is anova.
Output parameter  Description 

anova  Returns the actual anova table including mean squared, sum squared, df, F, and PR. 
model_accuracy  Returns model_accuracy statistics such as Rsquared, Fstatistic, Loglikelihood, Omnibus, and DurbinWatson. 
coefficients  Returns a table including the coefficient, standard deviation, tstatistics, Pvalue lower and upper bounds. 
Null hypothesis
User must provide a formula.
Syntax
 score anova formula=<string> type=<int> scale=<float> test=<fchisqcpnone> robust=<hc0hc1hc2hc3none> output=<anovamodel_accuracycoefficients>
Syntax constraints
 The field names of the arrays to work on are captured from the formula.
 The first array consists of a single field.
 The second array consists of a single field or multiple fields.
 Analysis of Variance (Anova) does not support the wildcard (*) character.
 The field names used in the formula cannot contain any of these special characters:
&%$#@!`\";<>^
Example
 inputlookup iris.csv  score anova formula="petal_length ~ sepal_length + sepal_length * sepal_width + sepal_width" output=anova
Example output
Example
 inputlookup iris.csv  score anova formula="petal_length ~ sepal_length + sepal_length * sepal_width + sepal_width" output=model_accuracy
Example output
Example
 inputlookup iris.csv  score anova formula="petal_length ~ sepal_length + sepal_length * sepal_width + sepal_width" output=coefficients
Example output
Augmented DickeyFuller (Adfuller)
You can use the Augmented DickeyFuller test to test for a unit root in a univariate process in the presence of serial correlation.
Implements statsmodels.tsa.stattools.adfuller
. Learn more here: https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html
Further reading: https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test
Parameters
 The
maxtag
parameter default is 10.  The
maxtag
parameter determines the maximum lag included in the test.  The
regression
parameter default is c.  The
regression
parameter determines the constant and trend order to include in the regression.c
: constant only (default)ct
: constant and trendctt
: constant, and linear and quadratic trendnc
: no constant, no trend
 The
autolag
parameter default is AIC. If None, then maxlag tags are used.
 If AIC or BIC, then the number of lags is chosed to minimize the corresponding information criterion.
 The parameter
stat
starts with maxlag and drops a lag until the tstatistic on the last lag length is significant using a 5%sized test.
 The
alpha
parameter default is 0.05.
Null hypothesis
The null hypothesis of the Augmented DickeyFuller is that there is a unit root, with the alternative that there is no unit root.
Syntax
score adfuller <field> autolag=<aicbictstatnone> regression=<cctcttnc> maxlag=<int> alpha=<float>
Syntax constraints
A single field.
Example
The following examples uses Augmented DickeyFuller on a test set.
 inputlookup app_usage.csv  score adfuller HR1
Example output
The following visualization shows Augmented DickeyFuller on a test set.
KolmogorovSmirnov (KS) test (1 sample)
You can use KolmogorovSmirnov (KS) test (1 sample) to test whether the specified field is statistically identical to the specified cumulative distribution function (cdf).
Implements scipy.stats.kstest
. Learn more here: https://docs.scipy.org/doc/scipy0.14.0/reference/generated/scipy.stats.kstest.html
Further reading: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
Parameters
Each cdf has a required set of parameters that must be specified.
Parameter  Required information 

cdf = chi2  df <int> loc <float> scale <float> 
cdf = longnorm  s <float> loc <float> scale <float> 
cdf = norm  loc <float> scale <float> 
Null hypothesis
The sample distribution is identical to the specified distribution (cdf, with cdf parameters).
Syntax
score kstest <field> cdf=<norm  lognorm  chi2> <required_cdf_parameters> alpha=<int>
All required cdf parameters must be supplied.
Syntax constraints
 A single field.
 KolmogorovSmirnov (KS) test (1 sample) does not support the wildcard (*) character.
Example
The following example uses KolmogorovSmirnov (KS) test (1 sample) on a test set.
 inputlookup power_plant.csv  score kstest Humidity cdf=norm loc=65 scale=2
Example output
The following visualization shows that you can reject the hypothesis that the field Humidity
is identical to a qfunction with mean 65 and standard deviation 2.
KolmogorovSmirnov (KS) test (2 samples)
Use the KolmogorovSmirnov statistic on two samples to test if two independent samples are drawn from the same distribution.
Implements scipy.stats.ttest_ind
. Learn more here: https://docs.scipy.org/doc/scipy0.14.0/reference/generated/scipy.stats.ks_2samp.html
Further reading: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Twosample%20Kolmogorov%E2%80%93Smirnov%20test
Parameters
The alpha
parameter default is 0.05.
Null hypothesis
KolmogorovSmirnov (KS) test (2 samples) is a twosided test for the null hypothesis that two independent samples are drawn from the same continuous distribution.
Syntax
score ks_2samp <a_field> against <b_field> alpha=<int>
Syntax constraints
A single pair of fields or a 1to1 comparison.
Example
The following example shows the two measurements of the HR
field are drawn from the same distribution.
 inputlookup app_usage.csv  score ks_2samp HR1 against HR2
Example output
The following example visualization rejects the null hypothesis that the two samples were drawn from the same distribution.
KwiatkowskiPhillipsSchmidtShin (KPSS)
Use the KwiatkowskiPhillipsSchmidtShin (KPSS) test to compute for the null hypothesis that a selected field is level or trend stationary.
Implements statsmodels.tsa.stattools.kpss
. Learn more here: https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.kpss.html
Further reading: https://en.wikipedia.org/wiki/KPSS_test
Parameters
 The
regression
parameter default is c. The
regression
parameter indicates the null hypothesis for the KPSS test.  The
c
parameter indicates that the data is stationary around a constant.  The
cf
parameter indicates that the data is stationary around a trend.
 The
 The
lags
parameter default is None. The
lags
parameter indicates the number of lags to be used. If None, set to int (12 * (n / 100)**(1 / 4)), where n is the number of samples.
 The
 The
alpha
default is 0.05.
Null hypothesis
The null hypothesis of the KPSS test is that the selected field (field
) is level or trend stationary.
Syntax
score kpss <field> regression=<c  ct> lags=<int> alpha=<float>
Syntax constraints
A single field.
Example
The following example uses KPSS test on a test set.
 inputlookup app_usage.csv  score kpss HR1
Example output
The following visualization shows KPSS test on a test set.
MannWhitneyU
You can use MannWhitneyU to test whether a randomly selected value from one sample is less than or greater than a randomly selected value from another sample.
Implements scipy.stats.mannwhitneyu
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html
Further reading: https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test
Parameters
 The
use continuity
parameter determines whether a continuity correction (1/2) must be taken into account.  The
use continuity
parameter default is True.  The
alternative
parameter determines whether to get the pvalue for the onesided hypothesis (less or greater) or for the twosided hypothesis (twosided).  The
alternative
parameter default is twosided.  The
alpha
parameter default is 0.05.
Null hypothesis
MannWhitneyU is a test of the null hypothesis that it is equally likely that a randomly selected value from one sample is less than or greater than a randomly selected value from another sample.
Syntax
score mannwhitneyu <a_field> against <b_field> use_continuity=<truefalse> alternative=<lesstwosidedgreater> alpha=<int>
Syntax constraints
A single pair of fields or a 1to1 comparison.
Example
The following example uses MannWhitneyU on a test set.
 inputlookup churn.csv  score mannwhitneyu "Day Charge" against "Eve Charge" alternative=greater
Example output
The following visualization shows that the random sample from Day Charge
is likely greater than a random sample in Eve Charge
.
Normaltest
you can use Normaltest to test whether a sample differs from a normal distribution.
Implements scipy.stats.normaltest
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html
Further reading:
 D'Agostino, R. B. (1971), "An omnibus test of normality for moderate and large sample size", Biometrika, 58, 341348
 D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from normality", Biometrika, 60, 613622
Parameters
The alpha
parameter is 0.05.
Null hypothesis
The sample <a_field_1>, ..., <a_field_n>
comes from a normal distribution.
Syntax
...score normaltest <field_1> <field_2> ... <field_n> alpha=<int>
An array (array_a) is specified by the ordered sequence of fields )<a1>, <a2>,...,<an>).
Syntax constraints
 Normaltest supports the wildcard (*) character.
 A single array or set of fields.
Example
The following example uses Normaltest on a test set.
 inputlookup diabetes.csv  score normaltest BMI age blood_pressure diabetes_pedigree glucose_concentration skin_thickness  table field pvalue
Example output
The following visualization tests whether fields are different from that of a normal distribution. From the plot you can deduce that glucose_concentration
is the most likely value to come from a normal distribution.
Oneway ANOVA
You can use Oneway ANOVA to test the null hypothesis that two or more groups have the same population mean.
Implements scipy.stats.f_oneway
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html
Further reading: https://en.wikipedia.org/wiki/Oneway_analysis_of_variance
Parameters
The alpha
parameter default is 0.05.
Null hypothesis
The specified groups field_1 ..., field_n
have the same population mean.
Syntax
score f_oneway <field_1> <field_2> ... <field_n> alpha=<int>
Syntax constraints
 Oneway ANOVA supports the wildcard (*) character.
 A single array or set of fields.
Example
The following example uses Oneway ANOVA on a test set.
 inputlookup app_usage.csv  score f_oneway HR1 HR2
Example output
The following visualization shows Oneway ANOVA on a test set.
Ttest (1 sample)
You can use Ttest (1 sample) to test whether the expected value (mean) of a sample of independent observations is equal to the specified population mean.
Implements scipy.stats.ttest_1samp
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html
Further reading: http://www.biostathandbook.com/onesamplettest.html
Parameters:
 The
popmean
parameter has no default, and must be specified.  The
popmean
parameter represents the population mean in the null hypothesis.  The
alpha
parameter default is 0.05.
Null hypothesis
The expected value (mean
) of the specified samples of independent observations (field_1 ... ,field_n
) are equal to the given population mean (popmean
).
Syntax
...score ttest_1samp <field_1> ... <field_n> popmean=<float> alpha=<int>
Syntax constraints
 Ttest (1 sample) supports the wildcard (*) character.
 A single array or set of fields.
Example
The following example tests whether the sample mean differs from an expected population mean.
 inputlookup power_plant.csv  score ttest_1samp Temperature popmean=20
Example output
The following visualization shows the negative statistic indicating that the sample mean is less than the hypothesized mean of 20.
Ttest (2 independent samples)
You can use Ttest (2 independent samples) to test whether two independent samples come from the same distribution.
Implements scipy.stats.ttest_ind
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html
Further reading: https://en.wikipedia.org/wiki/Student%27s_ttest#Independent_twosample_ttest
Parameters
 The
equal_var
parameter default is True.  If the
equal_var
parameter is True, perform a standard independent 2 sample test that assumes equal population variances.  If the
equal_var
parameter is False, perform Welch's Ttest, which does not assume equal population variance.  The
alpha
default is 0.05.
Null hypothesis
The null hypothesis is that the pairs a_field_i
and b_field_i
(independent) samples have identical average (expected) values. This test assumes that the fields have identical variances by default.
Syntax
score ttest_ind <a_field_1> ... <a_field_n> against <b_field_1>... <b_field_n> equal_var=<truefalse> alpha=<int>
Syntax constraints
 Two arrays specified by two ordered sequences of fields (1to1, nton, and 1ton comparison syntaxes).
 Ttest (2 independent samples) supports the wildcard (*) character in 1ton cases.
Example
The following example analyzes disk failures to see if disks are equally likely to fail, or if some disks are more likely to cause failure.
 inputlookup disk_failures.csv  score ttest_ind SMART_1_Raw against SMART_2_Raw SMART_3_Raw SMART_4_Raw
Disk failures are assumed to be independent across disks.
Example output
The following visualization shows that with an alpha of 0.05 you cannot reject the null hypothesis. It does not appear that disks 2, 3, and 4 are failing more than disk 1. All are close to each other.
You can use Ttest (2 related samples) to test if two related samples come from the same distribution.
Implements scipy.stats.ttest_ind
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html
Further reading: https://en.wikipedia.org/wiki/Student%27s_ttest#Dependent%20ttest%20for%20paired%20samples
Parameters
The alpha
parameter default is 0.05.
Null hypothesis
The null hypothesis is that the pairs a_field_i
and b_field_i
(related, as in two measurements of the same thing) samples have identical average (expected) values.
Syntax
score ttest_rel <a_field_1> ... <a_field_n> against <b_field_1> ... <b_field_n> alpha=<int>
Syntax constraints
 Two arrays specified by two ordered sequences of fields (1to1, nton and 1ton comparison syntaxes).
 Ttest (2 related samples) supports the wildcard (*) character in 1ton cases.
Example
The following example tests if the two measurements of the HR
field taken at the same time are statistically identical or not.
 inputlookup app_usage.csv  score ttest_rel HR1 against HR2
Example output
The following visualization shows that you can reject the null hypothesis and conclude that the two measurements are statistically different, potentially indicating a shift from equilibrium.
Wasserstein distance
You can use Wasserstein distance to compute the first wasserstein distance between two onedimensional distributions.
Using Wasserstein distance scoring requires running version 1.4 of the Python for Scientific Computing addon.
Implements scipy.stats.wasserstein_distance
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html
Further reading: https://en.wikipedia.org/wiki/Wasserstein_metric
Null hypothesis
The null hypothesis of the Wasserstein distance is that the a_field
and b_field
are probability distributions.
Syntax
 score wasserstein_distance <a_field> against <b_field>
Syntax constraints
 A single pair of fields or 1to1 comparison.
 Wasserstein distance does not support the wildcard (*) character.
Example
The following example shows the distance between two measurements of the HR field.
 inputlookup app_usage.csv  score wasserstein_distance HR1 against HR2
Example output
The following visualization shows Wasserstein distance on a test set.
Wilcoxon
You can use Wilcoxon to test if two related samples come from the same distribution.
Implements scipy.stats.wilcoxon
. Learn more here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html
Further reading: https://en.wikipedia.org/wiki/Wilcoxon_signedrank_test
Parameters
 The
zeromethod
parameter default is Wilcox.  Use the
pratt
parameter to include zero differences in ranking process (more conservative).  Use the
wilcox
parameter to discard zerodifferences.  Use the
zsplit
parameter to split zero ranks between positive and negative.  The
correction
parameter default is False.  If the
correction
parameter is True, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the zstatistic.  The
alpha
parameter default is 0.05.
Null hypothesis
The null hypothesis is that two related paired samples come from the same distribution. In particular, Wilcoxon tests whether the distribution of the differences x  y is symmetric about zero.
Syntax
score wilcoxon <a_field> against <b_field> zero_method=<pratt  wilcox  zsplit> correction=<True  False> alpha=<int>
Syntax constraints
A single pair of fields or 1to1 comparison.
Example
The following example shows you if the distribution of nighttime minutes used differs from the distribution of evening minutes used.
 inputlookup churn.csv  score wilcoxon "Night Mins" against "Eve Mins"
Example output
The following visualization shows the Wilcoxon test on a test set.
Kfold scoring
Crossvalidation assesses how well a statistical model generalizes on an independent dataset. Crossvalidation tells you how well your machine learning model is expected to perform on data that it has not been trained on. The scores obtained from Kfold crossvalidation are generally a less biased and less optimistic estimate of the model performance than a standard training and testing split.
There are many types of crossvalidation, but Kfold crossvalidation (kfold_cv
) is one of the most common.
The kfold_cv
parameter does not use the score
command, but operates like a scoring method.
Crossvalidation 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 hyperparameters on the same algorithm for choosing the best hyperparameters for a particular dataset.
 An improved method over a train/test split for quantifying model generalization.
Crossvalidation is not well suited for timeseries charts:
 In situations where the data is ordered such as timeseries, crossvalidation 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
With the kfold_cv
parameter, the training set is randomly partitioned into k equalsized subsamples. Then, each subsample takes a turn at becoming the validation (test) set, predicted by the other k1 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.
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 parameter 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 kfold is used instead of kfold. In stratified kfold, 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. In this way, the current implementation is seen as a scoring method.
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.
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This documentation applies to the following versions of Splunk^{®} Machine Learning Toolkit: 4.4.0, 4.4.1, 4.4.2
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