Machine Learning Functions in Expression Language

input data: Two dimensional array of data where: The first dimension (rows) specifies different data points. The second dimension (columns) specifies the feature values.
expected outcomes/labels: An array of expected outcomes for each row in the input data. Must be in the same order as the rows in the input data.
parameters: Additional parameters for the MLModel. Optional. NumberOfTrees - the number of trees in the random forest, default value is 10 SampleRatio - the proportion of samples used to train each of the trees in the decision forest, default value is 0.632

This pages describes functions and properties that are related to the machine learning functionality in the QPR ProcessAnalyzer expression language.

MLModel

MLModel properties	Description
Type	Returns the exact type of the MLModel.

DateTime functions	Parameters	Description
Train ()	Input data Expected outcomes Parameters	Trains given MLModel using given input data and expected outcomes. Parameters input data: Two dimensional array of data where: The first dimension (rows) specifies different data points. The second dimension (columns) specifies the feature values. expected outcomes/labels: An array of expected outcomes for each row in the input data. Must be in the same order as the rows in the input data. parameters: Additional parameters for the MLModel. Optional. NumberOfTrees - the number of trees in the random forest, default value is 10 SampleRatio - the proportion of samples used to train each of the trees in the decision forest, default value is 0.632 Returns the trained MLModel object itself.