Question

我尝试使用 split 函数来拆分数据集并迭代到它们中并预测每次迭代的混淆矩阵，并取所有混淆矩阵的平均值。我必须构建一个函数来这样做，它适用于我的其他项目，它的预测类为 (0,1)，但这不适用于 (1,2) 的预测类

这是我正在使用的数据的 head()

    Age Gender  TB  DB  Alkphos Sgpt    Sgot    TP  ALB A/G Class
0   65  Female  0.7 0.1 187 16  18  6.8 3.3 0.90    1
1   62  Male    10.9    5.5 699 64  100 7.5 3.2 0.74    1
2   62  Male    7.3 4.1 490 60  68  7.0 3.3 0.89    1
3   58  Male    1.0 0.4 182 14  20  6.8 3.4 1.00    1
4   72  Male    3.9 2.0 195 27  59  7.3 2.4 0.40    1

def total_confusion_matrix(model,x,y):
    total_matrix=[]
    cross = RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=1)
    for train_i, test_i in cross.split(x,y):
        x_train, x_test = x[train_i], x[test_i]
        y_train, y_test = y[train_i], y[test_i]
        model.fit(x_train, y_train)
        total_matrix.append(confusion_matrix(y_test, model.predict(x_test)))
    print('The total of confusion matrix in cross validation:')
    sns.heatmap(sum(total_matrix), annot=True)
    plt.title('Total Confusion Matrix')
    plt.show()
    print('\n The Mean confusion matrix in cross Validation:')
    sns.heatmap(sum(total_matrix)/len(total_matrix), annot=True)
    plt.title('Mean Confusion Matrix')
    plt.show()

total_confusion_matrix(rf_best,x,y)

KeyError: "None of [Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,  11,\n            ...\n            569, 570, 571, 572, 573, 574, 576, 577, 578, 580],\n           dtype='int64', length=464)] are in the [columns]"

我试图在交叉验证中获得所有混淆矩阵的平均值。我拆分了 cross_val 并获得了所有混淆矩阵

0 个答案: