在 Python 中使用 Sklearn 的逻辑回归函数

Question

我的逻辑回归函数有问题，我正在使用 Pycharm IDE 和 sklearn.linear_model 包 LogisticRegression.

我的调试器显示 AttributeError 'tuple' object has no attribute 'fit' and 'predict'。

代码如下：

def logistic_regression(df, y):
x_train, x_test, y_train, y_test = train_test_split(
    df, y, test_size=0.25, random_state=0)

sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)

clf = LogisticRegression(random_state=0, solver='sag',
                         penalty='l2', max_iter=1000, multi_class='multinomial'),
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)

return classification_metrics.print_metrics(y_test, y_pred, 'Logistic regression')

谁能帮忙找出这里的错误？因为对于其他功能，我尝试了 fit 和 predict 看起来不错。

Answer 1

我在评论中提到的代码中有一个小错误。

请删除逻辑回归模型对象创建中的逗号。

也没有这样的函数叫做classification_metrics.print_metrics

所以我用了metrics.classification_report

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
def logistic_regression(df, y):
    x_train, x_test, y_train, y_test = train_test_split(df, y, test_size=0.25, random_state=0)

    sc = StandardScaler()
    x_train = sc.fit_transform(x_train)
    x_test = sc.transform(x_test)

    clf = LogisticRegression(random_state=0, solver='sag', penalty='l2', max_iter=1000, multi_class='multinomial')
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)

    return metrics.classification_report(y_test, y_pred)

函数调用

logistic_regression(df, y)

在 Python 中使用 Sklearn 的逻辑回归函数

Logistic Regression Function Using Sklearn in Python

python

logistic-regression