Get the value of a given field. The list of all queryable fields is detailed below, and can be obtained programmatically using the
Field Description coefficients Regression coefficients convergence_threshold Desired solver accuracy feature_rescaling Bool indicating if features were rescaled during training features Feature column names l1_penalty l1 regularization weight l2_penalty l2 regularization weight lbfgs_memory_level LBFGS memory level (‘lbfgs only’) max_iterations Maximum number of solver iterations num_coefficients Number of coefficients in the model num_examples Number of examples used for training num_features Number of dataset columns used for training num_unpacked_features Number of features (including expanded list/dict features) solver Type of solver step_size Initial step size for the solver target Target column name training_iterations Number of solver iterations training_loss Residual sum-of-squares training loss training_rmse Training root-mean-squared-error (RMSE) training_solver_status Solver status after training training_time Training time (excludes preprocessing) unpacked_features Feature names (including expanded list/dict features) Parameters:
field : string
Name of the field to be retrieved.
out : [various]
The current value of the requested field.
>>> data = graphlab.SFrame('https://static.turi.com/datasets/regression/houses.csv')
>>> model = graphlab.linear_regression.create(data, target='price', features=['bath', 'bedroom', 'size']) >>> print model['num_features'] 3 >>> print model.get('num_features') # equivalent to previous line 3