NaiveBayesModel test

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test(self, frame, label_column, observation_columns=None)

[ALPHA] Predict test frame labels and return metrics.

Parameters:

frame : Frame

A frame whose labels are to be predicted. By default, predict is run on the same columns over which the model is trained.

label_column : unicode

Column containing the actual label for each observation.

observation_columns : list (default=None)

Column(s) containing the observations whose labels are to be predicted. By default, we predict the labels over columns the NaiveBayesModel was trained on.

Returns:

: dict

A dictionary with binary classification metrics. The data returned is composed of the following keys:

‘accuracy’ : double

The proportion of predictions that are correctly identified

‘confusion_matrix’ : dictionary

A table used to describe the performance of a classification model

‘f_measure’ : double

The harmonic mean of precision and recall

‘precision’ : double

The proportion of predicted positive instances that are correctly identified

‘recall’ : double

The proportion of positive instances that are correctly identified.

Predict the labels for a test frame and run classification metrics on predicted and target labels.

Examples

See here for examples.