turicreate.logistic_classifier.LogisticClassifier.evaluate

LogisticClassifier.evaluate(dataset, metric='auto', missing_value_action='auto', with_predictions=False)

Evaluate the model by making predictions of target values and comparing these to actual 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 (micro average).
  • ‘auc’ : Area under the ROC curve (macro average)
  • ‘precision’ : Precision score (macro average)
  • ‘recall’ : Recall score (macro average)
  • ‘f1_score’ : F1 score (macro average)
  • ‘log_loss’ : Log loss
  • ‘confusion_matrix’ : An SFrame with counts of possible prediction/true label combinations.
  • ‘roc_curve’ : An SFrame containing information needed for an ROC curve

For more flexibility in calculating evaluation metrics, use the evaluation module.

missing_value_action : str, optional

Action to perform when missing values are encountered. This can be one of:

  • ‘auto’: Default to ‘impute’
  • ‘impute’: Proceed with evaluation by filling in the missing values with the mean of the training data. Missing values are also imputed if an entire column of data is missing during evaluation.
  • ‘error’: Do not proceed with evaluation and terminate with an error message.
Returns:
out : dict

Dictionary of evaluation results where the key is the name of the evaluation metric (e.g. accuracy) and the value is the evaluation score.

See also

create, predict, classify

Examples

>>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
>>> data['is_expensive'] = data['price'] > 30000
>>> model = turicreate.logistic_classifier.create(data,
...                             target='is_expensive',
...                             features=['bath', 'bedroom', 'size'])
>>> results = model.evaluate(data)
>>> print results['accuracy']