Python 使用多个预测变量的逻辑回归

发布于06月09日

我想拟合一个逻辑回归模型，用X1和X2预测Y.

x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size)

然后

model = LogisticRegression()
model.fit(x_train,y_train)

要使用X预测Y，我不知道如何使用多个预测器来训练数据.需要帮忙吗？

推荐答案

如果有2个特征X1和X2，则训练数据X将有2列.例如，如果数据有X10 X1和X10 X2，则X的形状应为(X10 x 2)

例如，您有一个包含3列的csv文件:X1、X2、y

import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

df = pd.read_csv('my_file.csv')
X = df[['X1', 'X2']]
Y = df['y']

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
model = LogisticRegression()
model.fit(x_train,y_train)

y_pred = model.predict(x_test)

acc = accuracy_score(y_test, y_pred)