SvmModel train

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train(self, frame, label_column, observation_columns, intercept=True, num_iterations=100, step_size=1.0, reg_type=None, reg_param=0.01, mini_batch_fraction=1.0)

[ALPHA] Build SVM with SGD model

Parameters:

frame : Frame

A frame to train the model on.

label_column : unicode

Column name containing the label for each observation.

observation_columns : list

List of column(s) containing the observations.

intercept : bool (default=True)

Flag indicating if the algorithm adds an intercept. Default is true.

num_iterations : int32 (default=100)

Number of iterations for SGD. Default is 100.

step_size : float64 (default=1.0)

Initial step size for SGD optimizer for the first step. Default is 1.0.

reg_type : unicode (default=None)

Regularization “L1” or “L2”. Default is “L2”.

reg_param : float64 (default=0.01)

Regularization parameter. Default is 0.01.

mini_batch_fraction : float64 (default=1.0)

Set fraction of data to be used for each SGD iteration. Default is 1.0; corresponding to deterministic/classical gradient descent.

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

Examples

See here for examples.