混淆矩阵是一个矩阵，类别个数可以有多个，a[i][j]表示将类别i的样本误判为类别j的个数。

classification_report用来分析不同类别的准确率，召回率，F1值等，从而便于按照类别查看准确率、召回率。

总体的正确率跟classification_report中的正确率是不一样。

import numpy as npimport sklearn.metrics as metricsdef report(mine, real):    if len(mine) != len(real):        print("mine和real长度不一样")        exit(0)    all_classes = set(list(mine) + list(real))    precision = dict()    recall = dict()    f1 = dict()    support = dict()    for c in all_classes:        if np.count_nonzero(mine == c):            precision[c] = np.count_nonzero(np.logical_and(mine == real, real == c)) / np.count_nonzero(mine == c)        else:            precision[c] = 0        if np.count_nonzero(real == c):            recall[c] = np.count_nonzero(np.logical_and(mine == real, real == c)) / np.count_nonzero(real == c)        else:            recall[c] = 0        if precision[c] and recall[c]:            f1[c] = 2 / (1 / precision[c] + 1 / recall[c])        else:            f1[c] = 0        support[c] = np.count_nonzero(real_ans == c)    s = ''    s += "%10s%10s%10s%10s%10s\n" % ("class", "precision", "recall", "f1", "support")    fmtstr2 = "%10s%10.2f%10.2f%10.2f%10d\n"    for c in all_classes:        s += (fmtstr2 % (c, precision[c], recall[c], f1[c], support[c]))    s += fmtstr2 % ("avg",                    np.sum([precision[c] * support[c] for c in all_classes]) / len(mine),                    np.sum([recall[c] * support[c] for c in all_classes]) / len(mine),                    np.sum([f1[c] * support[c] for c in all_classes]) / len(mine),                    len(mine)                    )    return smy_ans = np.random.randint(0, 2, 10)real_ans = np.random.randint(0, 2, 10)print(my_ans)print(real_ans)print("分类报告是按照类别分开的")print('=' * 10)print(metrics.classification_report(real_ans, my_ans))print('=' * 10)print(report(my_ans, real_ans))print("准确率跟上面的正确率不一样")print(metrics.accuracy_score(real_ans, my_ans))print(np.count_nonzero(my_ans == real_ans) / len(my_ans))