与Kfold交叉验证

Question

我正在尝试使用与银行业历史相关的三个二进制解释变量：默认，住房和贷款，以使用Logistic回归分类器预测二进制响应变量。

我有以下数据集：

映射功能，将文本（是/是）转换为整数0/1

convert_to_binary = {'no' : 0, 'yes' : 1}
default = bank['default'].map(convert_to_binary)
housing = bank['housing'].map(convert_to_binary)
loan = bank['loan'].map(convert_to_binary)
response = bank['response'].map(convert_to_binary)

我添加了三个解释变量并响应数组

data = np.array([np.array(default), np.array(housing), np.array(loan),np.array(response)]).T

kfold = KFold(n_splits=3)

scores = []
for train_index, test_index in kfold.split(data):
    X_train, X_test = data[train_index], data[test_index]
    y_train, y_test = response[train_index], response[test_index]
    model = LogisticRegression().fit(X_train, y_train)
    pred = model.predict(data[test_index])
    results = model.score(X_test, y_test)
    scores.append(results)
print(np.mean(scores))

我的准确性始终是100％，我知道这是不正确的。准确性应该在50-65％左右？

我在做错什么吗？

Answer 1

分割不正确

这是正确的拆分

X_train, X_labels = data[train_index], response[train_index]
y_test, y_labels = data[test_index], response[test_index]
model = LogisticRegression().fit(X_train, X_labels)
pred = model.predict(y_test)
acc = sklearn.metrics.accuracy_score(y_labels,pred,normalize=True)