LassoModel train

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train(self, frame, value_column, observation_columns, initial_weights=None, num_iterations=500, step_size=1.0, reg_param=0.01, mini_batch_fraction=1.0)

Train Lasso 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.

initial_weights : list (default=None)

Initial set of weights to be used. List should be equal in size to the number of features in the data.

num_iterations : int32 (default=500)

Number of iterations of gradient descent to run

step_size : float64 (default=1.0)

Step size scaling to be used for the iterations of gradient descent.

reg_param : float64 (default=0.01)

regParam Regularization parameter.

mini_batch_fraction : float64 (default=1.0)

Fraction of data to be used per iteration.

Returns:

: dict

Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number

• of iterations of gradient descent using the specified step size. Each iteration uses
• miniBatchFraction fraction of the data to calculate a stochastic gradient. The weights used
• in gradient descent are initialized using the initial weights provided.

Examples

See here for examples.