python sklearn机械学习模型-回归

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安装

数据

使用

线性回归

决策树回归

随机森林回归

岭回归

套索回归

支持向量机回归

总结

安装

pip install scikit-learn

数据

X,y即为所需要进行回归处理的数据。

操作：拆分为训练集和测试集

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=12)

使用

线性回归

# 线性回归模型
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 创建线性回归模型并拟合
model = LinearRegression()
model.fit(X_train, y_train)# 进行预测
y_pred = model.predict(X_test)# 计算模型性能指标
# 利用均方误差(MSE)评价预测结果的合理性，MSE的数值越小越好，即越接近0表示模型的预测与真实值之间的差异较小。
mse = mean_squared_error(y_test, y_pred)  
# 利用平均绝对误差(MAE)预测结果的合理性，MAE的数值越小越好，即越接近0表示模型的预测与真实值之间的差异较小。
mae = mean_absolute_error(y_test, y_pred)  
# r2分数越接近1代表模型性能越好
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')

决策树回归

# 决策树回归模型
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 创建决策树回归模型并拟合
model = DecisionTreeRegressor()
model.fit(X_train, y_train)# 进行预测
y_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)  
mae = mean_absolute_error(y_test, y_pred)  
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')

随机森林回归

# 随机森林回归模型
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 创建随机森林回归模型并拟合
model = RandomForestRegressor()
model.fit(X_train, y_train)# 进行预测
y_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)  
mae = mean_absolute_error(y_test, y_pred)  
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')

岭回归

# 岭回归模型
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 创建岭回归模型
ridge = Ridge()# 定义alpha值的候选范围
param_grid = {'alpha':[0.1,1.0,10.0]}# 使用交叉验证选择最优的alpha值
ridge_cv = GridSearchCV(ridge,param_grid,cv=5)
ridge_cv.fit(X_train,y_train)# 获取最优的alpha值
best_alpha = ridge_cv.best_params_['alpha']
print("最优alpha值：", best_alpha)# 使用最优的alpha值创建并训练岭回归模型
ridge = Ridge(alpha=best_alpha)
ridge.fit(X_train,y_train)# 进行预测
y_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)  
mae = mean_absolute_error(y_test, y_pred)  
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')

套索回归

# 套索回归模型
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 定义alpha值候选范围
param_grid = {'alpha':[0.1,1.0,10.0]}# 使用交叉验证选择最优的alpha值
ridge_cv = GridSearchCV(ridge,param_grid,cv=5)
ridge_cv.fit(X_train,y_train)# 获取最优的alpha值
best_alpha = ridge_cv.best_params_['alpha']
print("最优alpha值：", best_alpha)# 创建并训练套索回归模型
lasso = Lasso(alpha=best_alpha)
lasso.fit(X_train,y_train)# 在测试集上进行预测
y_pred = lasso.predict(X_test)mse = mean_squared_error(y_test, y_pred)  
mae = mean_absolute_error(y_test, y_pred)  
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')

支持向量机回归

# 支持向量机回归
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score# 特征标准化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)# 创建支持向量机回归模型并拟合
model = SVR()
model.fit(X_train_scaled, y_train)# 进行预测
y_pred = model.predict(X_test_scaled)mse = mean_squared_error(y_test, y_pred)  
mae = mean_absolute_error(y_test, y_pred)  
r2 = r2_score(y_test, y_pred)  print(f'Mean Squared Error: {mse:.4f}')  
print(f'Mean Absolute Error: {mae:.4f}')  
print(f'R^2 Score: {r2:.4f}')