1. Component library
  2. Analyze
  3. Machine learning
  4. Train Random Forest regression

Train Random Forest regression

Train a regression model of the 'Random Forest' type.

Analyzer

Result metrics (MLRegressionAnalyzerResult)

Configured properties

  • Cross validation sample rate

    Determine how much (if any) of the records should be used for cross-validation.

    Percentage Required

  • Feature modifier types

    List of Choice: Scaled (Min-Max) Direct (0.0 to 1.0) Direct (1 or 0) Vector (One Hot Encoding) Vector (2-gram) Vector (3-gram) Vector (4-gram) Vector (5-gram) Mapped with Features Required

  • Features

    List of InputColumn<Object> Required

  • Include unique value features

    Include generated features that are only triggered once in the training data set.

    boolean Required

  • Max features generated per column

    Defines the maximum number of features to generate per column. Applies to feature vectors such as 'One-Hot Encoding' or n-grams.

    Integer Required

  • Number of trees

    int Required

  • Regression output

    InputColumn<Number> Required

  • Save model to file

    File Optional