Assistants overview

There are 6 Assistants that live within the Machine Learning Toolkit. These Assistants enable the creation of machine learning models through a guided user interface.

Through the Assistants workflow users specify data sources, select an algorithm and algorithm parameters , select the fields for the algorithms to analyze and set training/test data splits. After the user instructs the Assistant to fit the algorithms to the selected training data and generate results, the workflow continues through the available visualizations and statistical analysis. Every step of the workflow has tooltips as additional guides, the option to see the SPL being written (with an explanation for the commands) by the Assistant, and an option to open a clone of the SPL in a new search window for customization by the user.

Choose the assistant to suit your needs:

• The Predict Numeric Fields Assistant uses regression algorithms to predict or estimate numeric values. Such models are useful for determining to what extent certain peripheral factors contribute to a particular metric result. After the regression model is computed, you can use these peripheral values to make a prediction on the metric result.

• The Predict Categorical Fields Assistant displays a type of learning known as classification. A classification algorithm learns the tendency for data to belong to one category or another based on related data.

• The Detect Numeric Outliers Assistant determines values that appear to be extraordinarily higher or lower than the rest of the data. Identified outliers are indicative of interesting, unusual, and possibly dangerous events. This assistant is restricted to one numeric data field.

• The Detect Categorical Outliers Assistant identifies data that indicate interesting or unusual events. This assistant allows non-numeric and multi-dimensional data, such as string identifiers and IP addresses. To detect categorical outliers, input data and select the fields for which to look for unusual combinations or a coincidence of rare values. When multiple fields have rare values, the result is an outlier.

• The Forecast Time Series Assistant forecasts the next values in a sequence for a single time series. The result includes both the forecasted value and a measure of the uncertainty of that forecast. Forecasting refers to the use of past time series data trends to make a prediction about likely future values.

• The Cluster Numeric Events Assistant partitions events with multiple numeric fields into groups of events based on the values of those fields. The groupings aren't known in advance an the algorithms are often referred to as unsupervised learning.