turicreate.nearest_neighbor_classifier.NearestNeighborClassifier.evaluate — Turi Create API 6.4.1 documentation (original) (raw)

NearestNeighborClassifier. evaluate(dataset, metric='auto', max_neighbors=10, radius=None)¶

Evaluate the model’s predictive accuracy. This is done by predicting the target class for instances in a new dataset and comparing to known target values.

Parameters:	dataset : SFrame Dataset of new observations. Must include columns with the same names as the target and features used for model training. Additional columns are ignored. metric : str, optional Name of the evaluation metric. Possible values are: ‘auto’: Returns all available metrics. ‘accuracy’: Classification accuracy. ‘confusion_matrix’: An SFrame with counts of possible prediction/true label combinations. ‘roc_curve’: An SFrame containing information needed for an roc curve (binary classification only). max_neighbors : int, optional Maximum number of neighbors to consider for each point. radius : float, optional Maximum distance from each point to a neighbor in the reference dataset.
Returns:	out : dict Evaluation results. The dictionary keys are accuracy and_confusion_matrix_ and roc_curve (if applicable).

Parameters:

dataset : SFrame Dataset of new observations. Must include columns with the same names as the target and features used for model training. Additional columns are ignored. metric : str, optional Name of the evaluation metric. Possible values are: ‘auto’: Returns all available metrics. ‘accuracy’: Classification accuracy. ‘confusion_matrix’: An SFrame with counts of possible prediction/true label combinations. ‘roc_curve’: An SFrame containing information needed for an roc curve (binary classification only). max_neighbors : int, optional Maximum number of neighbors to consider for each point. radius : float, optional Maximum distance from each point to a neighbor in the reference dataset.

Returns:

out : dict Evaluation results. The dictionary keys are accuracy and_confusion_matrix_ and roc_curve (if applicable).

Notes

Because the model randomly breaks ties between predicted classes, the results of repeated calls to evaluate method may differ.

Examples

sf_train = turicreate.SFrame({'species': ['cat', 'dog', 'fossa', 'dog'], ... 'height': [9, 25, 20, 23], ... 'weight': [13, 28, 33, 22]}) m = turicreate.nearest_neighbor_classifier.create(sf, target='species') ans = m.evaluate(sf_train, max_neighbors=2, ... metric='confusion_matrix') print ans['confusion_matrix'] +--------------+-----------------+-------+ | target_label | predicted_label | count | +--------------+-----------------+-------+ | cat | dog | 1 | | dog | dog | 2 | | fossa | dog | 1 | +--------------+-----------------+-------+