bigframes.ml.metrics.accuracy_score

bigframes.ml.metrics.accuracy_score(y_true: DataFrame | Series, y_pred: DataFrame | Series, *, normalize=True) → float

Accuracy classification score.

Examples:

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

>>> y_true = bpd.DataFrame([0, 2, 1, 3])
>>> y_pred = bpd.DataFrame([0, 1, 2, 3])
>>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred)
>>> accuracy_score
np.float64(0.5)

If False, return the number of correctly classified samples:

>>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred, normalize=False)
>>> accuracy_score
np.int64(2)

Parameters:

• y_true (Series or DataFrame of shape (n_samples,)) – Ground truth (correct) labels.

• y_pred (Series or DataFrame of shape (n_samples,)) – Predicted labels, as returned by a classifier.

• normalize (bool, default True) – Default to True. If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.

Returns:

If normalize == True, return the fraction of correctly

classified samples (float), else returns the number of correctly classified samples (int).

Return type:

float