python实现房价预测，采用回归和随机梯度下降法

最新推荐文章于 2025-10-24 16:07:14 发布

原创

最新推荐文章于 2025-10-24 16:07:14 发布 · 5.5k 阅读

7 ·

CC 4.0 BY-SA版权

该博客通过Python的sklearn库展示了如何利用线性回归（LinearRegression）和随机梯度下降（SGDRegressor）模型对波士顿房价数据集进行预测。首先，数据被划分为训练集和测试集，然后使用标准化预处理。接着，分别训练两种模型，并计算它们在测试集上的默认评估指标、R方分数、均方误差和平均绝对误差，以比较其预测性能。

from sklearn.datasets import load_boston

boston = load_boston()

from sklearn.cross_validation import train_test_split

import numpy as np;

X = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)

print 'The max target value is: ', np.max(boston.target)
print 'The min target value is: ', np.min(boston.target)
print 'The average terget value is: ', np.mean(boston.target)

from sklearn.preprocessing import StandardScaler

ss_X = StandardScaler()
ss_y = StandardScaler()

X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)

from sklearn.linear_model import LinearRegression

lr = LinearRegression()

lr.fit(X_train, y_train)

lr_y_predict = lr.predict(X_test)

from sklearn.linear_model import SGDRegressor

sgdr = SGDRegressor()

sgdr.fit(X_train, y_train)

sgdr_y_predict = sgdr.predict(X_test)

p

最低0.47元/天解锁文章