机器学习分类模型-线性回归Linear regression

"""Linear regression module."""
from collections import Counter
import numpy as np
import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split  # 这里是引用了交叉验证
from sklearn.linear_model import LinearRegression


if __name__ == '__main__':
    my_data = pd.read_csv('D:\\Anaconda3\\pythonProject\\Train_predict\\code\\Data\\totaldata_shuffle.csv')
    X = my_data.iloc[:,:33]
    y = my_data.iloc[:,33]

    # SMOTH过采样
    X_resampled, y_resampled = SMOTE().fit_resample(X, y)
    Counter(y_resampled)

    # 交叉验证，选择20%为测试集
    X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=532)

    model = LinearRegression()
    model.fit(X_train, y_train)
    print('-' * 100)

    # 预测
    y_predict = model.predict(X_test)
    y_predict_class = []
    for yi in y