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各类竞赛中，最常见的一个准确度指标就是
sklearn.metrics.accuracy_score¶
。该指标浅显易懂，下面作简要介绍
1.示例
#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
@author:livingbody
@file:accuracy_score.py
@time:2022/08/29
"""
from sklearn.metrics import accuracy_score
if __name__ == '__main__':
y_pred = [0, 2, 1, 3, 4]
y_true = [0, 1, 2, 3, 4]
acc = accuracy_score(y_true, y_pred)
print(f"acc: {acc}")
acc: 0.6
如上所示，5个值得情况下，错2个，准确率60%，浅显易懂。
2.api介绍
sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)
- 可用来计算分类准确率分数。
- 可用来计算多分类准确率分数。
"""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, default=True
If ``False``, return the number of correctly classified samples.
Otherwise, return the fraction of correctly classified samples.
sample_weight : array-like of shape (n_samples,), default=None
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``.
3.多分类准确率分数计算
import numpy as np
accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5
