2019-11-29

from sklearn.metrics import accuracy_score

help(accuracy_score)

Help on function accuracy_score in module sklearn.metrics.classification:

accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

    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 :ref:`User Guide <accuracy_score>`.

    Parameters

    ———-

    y_true : 1d array-like, or label indicator array / sparse matrix

        Ground truth (correct) labels.

    y_pred : 1d array-like, or label indicator array / sparse matrix

        Predicted labels, as returned by a classifier.

    normalize : bool, optional (default=True)

        If “False“, return the number of correctly classified samples.

        Otherwise, return the fraction of correctly classified samples.

ormalize:bool,可选(默认值=True),如果是`False‘,则返回正确分类的样本数。否则,返回正确分类样本的分数。

    sample_weight : array-like of shape = [n_samples], optional

        Sample weights.

    Returns

    ——-

    score : float

        If “normalize == True“, return the fraction of correctly

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

        classified samples (int).

        The best performance is 1 with “normalize == True“ and the number

        of samples with “normalize == False“.

    See also

    ——–

    jaccard_score, hamming_loss, zero_one_loss

    Notes

    —–

    In binary and multiclass classification, this function is equal

    to the “jaccard_score“ function.

    Examples

    ——–

from sklearn.metrics import accuracy_score

y_pred = [0, 2, 1, 3]

y_true = [0, 1, 2, 3]

accuracy_score(y_true, y_pred)

    0.5

accuracy_score(y_true, y_pred, normalize=False)

    2

    In the multilabel case with binary label indicators:

import numpy as np

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

    0.5

https://www.jianshu.com/p/3a8526a57232

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