turicreate.evaluation.precision¶

turicreate.evaluation.precision(targets, predictions, average='macro')¶

Compute the precision score for classification tasks. The precision score quantifies the ability of a classifier to not label a negative example as positive. The precision score can be interpreted as the probability that a positive prediction made by the classifier is positive. The score is in the range [0,1] with 0 being the worst, and 1 being perfect.

The precision score is defined as the ratio:: \[\frac{tp}{tp + fp}\]

where tp is the number of true positives and fp the number of false positives.

Parameters:

targets : SArray

Ground truth class labels.

predictions : SArray

The prediction that corresponds to each target value. This SArray must have the same length as targets and must be of the same type as the targets SArray.

average : string, [None, ‘macro’ (default), ‘micro’]

Metric averaging strategies for multiclass classification. Averaging strategies can be one of the following:

None: No averaging is performed and a single metric is returned for each class.

‘micro’: Calculate metrics globally by counting the total true positives, and false positives.

‘macro’: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

Returns:

out : float (for binary classification) or dict[float]: Score for the positive class (for binary classification) or an average score for each class for multi-class classification. If average=None, then a dictionary is returned where the key is the class label and the value is the score for the corresponding class label.

See also

confusion_matrix, accuracy, recall, f1_score

Notes

For binary classification, when the target label is of type “string”, then the labels are sorted alphanumerically and the largest label is chosen as the “positive” label. For example, if the classifier labels are {“cat”, “dog”}, then “dog” is chosen as the positive label for the binary classification case.

Examples

# Targets and Predictions
>>> targets = turicreate.SArray([0, 1, 2, 3, 0, 1, 2, 3])
>>> predictions = turicreate.SArray([1, 0, 2, 1, 3, 1, 0, 1])

# Micro average of the precision scores for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                               average = 'micro')
0.25

# Macro average of the precision scores for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                              average = 'macro')
0.3125

# Precision score for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                               average = None)
{0: 0.0, 1: 0.25, 2: 1.0, 3: 0.0}

This metric also works for string classes.

# Targets and Predictions
>>> targets = turicreate.SArray(
...      ["cat", "dog", "foosa", "snake", "cat", "dog", "foosa", "snake"])
>>> predictions = turicreate.SArray(
...      ["dog", "cat", "foosa", "dog", "snake", "dog", "cat", "dog"])

# Micro average of the precision scores for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                               average = 'micro')
0.25

# Macro average of the precision scores for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                              average = 'macro')
0.3125

# Precision score for each class.
>>> turicreate.evaluation.precision(targets, predictions,
...                               average = None)
{0: 0.0, 1: 0.25, 2: 1.0, 3: 0.0}