LinearRegressionModel train

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train(self, frame, value_column, observation_columns, elastic_net_parameter=0.0, fit_intercept=True, max_iterations=100, reg_param=0.0, standardization=True, tolerance=1e-06)

Build linear regression model.

Parameters:

frame : Frame

A frame to train the model on

value_column : unicode

Column name containing the value for each observation.

observation_columns : list

List of column(s) containing the observations.

elastic_net_parameter : float64 (default=0.0)

Parameter for the ElasticNet mixing. Default is 0.0

fit_intercept : bool (default=True)

Parameter for whether to fit an intercept term. Default is true

max_iterations : int32 (default=100)

Parameter for maximum number of iterations. Default is 100

reg_param : float64 (default=0.0)

Parameter for regularization. Default is 0.0

standardization : bool (default=True)

Parameter for whether to standardize the training features before fitting the model. Default is true

tolerance : float64 (default=1e-06)

Parameter for the convergence tolerance for iterative algorithms. Default is 1E-6

Returns:

: dict

Trained linear regression model

Creating a LinearRegression Model using the observation column and target column of the train frame

Examples

See here for examples.