bigframes.ml.metrics.precision_score#

Compute the precision.

The precision is the ratio tp / (tp + fp), where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

The best value is 1 and the worst value is 0.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics

>>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2])
>>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1])
>>> precision_score = bigframes.ml.metrics.precision_score(y_true, y_pred, average=None)
>>> precision_score
0    0.666667
1    0.000000
2    0.000000
dtype: float64

Parameters:

y_true – Series or DataFrame of shape (n_samples,) Ground truth (correct) target values.
y_pred – Series or DataFrame of shape (n_samples,) Estimated targets as returned by a classifier.
average – {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’ This parameter is required for multiclass/multilabel targets. Possible values are ‘None’, ‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’. Only None and ‘binary’ is supported.

Returns:

float (if average is not None) or Series of float of shape (n_unique_labels,).: Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task.

Return type:

precision

bigframes.ml.metrics.precision_score#

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