Python底层技术揭秘：如何实现模型训练和预测

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV

# 数据准备
X_train, y_train = ...

# 线性回归模型
lr = LinearRegression()

# 超参数
params = {
    "fit_intercept": [True, False],
    "nORMalize": [True, False]
}

# 网格搜索
grid = GridSearchCV(lr, params, cv=5)
grid.fit(X_train, y_train)

# 最佳超参数
best_params = grid.best_params_
print(best_params)

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

# 数据准备
X_train, y_train = ...

# 线性回归模型
lr = LinearRegression()

# 交叉验证
scores = cross_val_score(lr, X_train, y_train, cv=5)
mean_score = scores.mean()
print(mean_score)

from sklearn.linear_model import LinearRegression

# 数据准备
X_train, y_train = ...

# 线性回归模型
lr = LinearRegression(fit_intercept=True, normalize=False)

# 训练模型
lr.fit(X_train, y_train)

from sklearn.linear_model import LinearRegression

# 数据准备
X_test = ...

# 线性回归模型
lr = LinearRegression(fit_intercept=True, normalize=False)

# 预测
y_pred = lr.predict(X_test)
print(y_pred)