dask_ml.metrics.accuracy_score

dask_ml.metrics.accuracy_score¶

dask_ml.metrics.accuracy_score(y_true: dask_ml._typing.ArrayLike, y_pred: dask_ml._typing.ArrayLike, normalize: bool = True, sample_weight: Optional[dask_ml._typing.ArrayLike] = None, compute: bool = True) → dask_ml._typing.ArrayLike¶

Accuracy classification score.

In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

Read more in the User Guide.

Parameters

y_true1d array-like, or label indicator array: Ground truth (correct) labels.
y_pred1d array-like, or label indicator array: Predicted labels, as returned by a classifier.
normalizebool, optional (default=True): If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.
sample_weight1d array-like, optional: Sample weights.

New in version 0.7.0.

Returns

scorescalar dask Array

If normalize == True, return the correctly classified samples (float), else it returns the number of correctly classified samples (int).

The best performance is 1 with normalize == True and the number of samples with normalize == False.

Notes

In binary and multiclass classification, this function is equal to the jaccard_similarity_score function.

Examples

>>> import dask.array as da
>>> import numpy as np
>>> from dask_ml.metrics import accuracy_score
>>> y_pred = da.from_array(np.array([0, 2, 1, 3]), chunks=2)
>>> y_true = da.from_array(np.array([0, 1, 2, 3]), chunks=2)
>>> accuracy_score(y_true, y_pred)
dask.array<mean_agg-aggregate, shape=(), dtype=float64, chunksize=()>
>>> _.compute()
0.5
>>> accuracy_score(y_true, y_pred, normalize=False).compute()
2

In the multilabel case with binary label indicators:

>>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5

dask_ml.metrics.r2_score

dask_ml.metrics.log_loss