LinearRegressionModel train

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train(self, frame, label_column, observation_columns, intercept=None, num_iterations=None, step_size=None, reg_type=None, reg_param=None, mini_batch_fraction=None)

[ALPHA] Build linear regression model.

Parameters:

frame : <bound method AtkEntityType.__name__ of <trustedanalytics.rest.jsonschema.AtkEntityType object at 0x7f9e686f3fd0>>

A frame to train the model on.

label_column : unicode

Column name containing the label for each observation.

observation_columns : list

Column(s) containing the observations.

intercept : bool (default=None)

The algorithm adds an intercept. Default is true.

num_iterations : int32 (default=None)

Number of iterations. Default is 100.

step_size : int32 (default=None)

Step size for optimizer. Default is 1.0.

reg_type : unicode (default=None)

Regularization L1 or L2. Default is L2.

reg_param : float64 (default=None)

Regularization parameter. Default is 0.01.

mini_batch_fraction : float64 (default=None)

Mini batch fraction parameter. Default is 1.0.

Returns:

: _Unit

Creating a Linear Regression Model using the observation column and label column of the train frame.

Examples

>>> my_model = ta.LinearRegressionModel(name='LinReg')
>>> my_model.train(train_frame, 'name_of_label_column',['name_of_observation_column(s)'],false, 50, 1.0, "L1", 0.02, 1.0)