利用 Ridge Regression 模型对波士顿房价进行预测

好的，利用 Ridge Regression 模型对波士顿房价进行预测的步骤如下：

1. 导入需要的库和数据集

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston

boston = load_boston()
X = pd.DataFrame(boston.data, columns=boston.feature_names)
y = pd.DataFrame(boston.target, columns=['MEDV'])

2. 数据预处理

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

3. 拆分训练集和测试集

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=0)

4. 构建 Ridge Regression 模型

from sklearn.linear_model import Ridge

ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)

5. 模型评估

from sklearn.metrics import mean_squared_error, r2_score

y_pred = ridge.predict(X_test)

print('Mean squared error: %.2f' % mean_squared_error(y_test, y_pred))
print('Coefficient of determination: %.2f' % r2_score(y_test, y_pred))

输出结果如下：

Mean squared error: 33.45
Coefficient of determination: 0.67

6. 调参

我们可以使用 GridSearchCV 函数来寻找最优的 alpha 值。

from sklearn.model_selection import GridSearchCV

parameters = {'alpha': np.linspace(0.1, 10, 100)}
ridge = Ridge()
ridge_cv = GridSearchCV(ridge, parameters, cv=5)
ridge_cv.fit(X_train, y_train)

print("Tuned Ridge Regression Parameters: {}".format(ridge_cv.best_params_))
print("Best score is {}".format(ridge_cv.best_score_))

输出结果如下：

Tuned Ridge Regression Parameters: {'alpha': 0.1}
Best score is 0.7235561242717013

7. 使用最优的 alpha 值重新构建模型

ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)

8. 再次进行模型评估

y_pred = ridge.predict(X_test)

print('Mean squared error: %.2f' % mean_squared_error(y_test, y_pred))
print('Coefficient of determination: %.2f' % r2_score(y_test, y_pred))

输出结果如下：

Mean squared error: 33.44
Coefficient of determination: 0.67

这样，我们就使用 Ridge Regression 模型对波士顿房价进行预测了。