python机器学习12 正则化线性回归(波士顿房价预测)

正则化线性回归(波士顿房价预测)

导入模块

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.font_manager import FontProperties

from sklearn.linear_model import Lasso

from sklearn.linear_model import Ridge

from sklearn.linear_model import ElasticNet

%matplotlib inline

font = FontProperties(fname='/Library/Fonts/Heiti.ttc')

获取数据

df = pd.read_csv('housing-data.txt', sep='\s+', header=0)

X = df[['RM']].values

y = df['MEDV'].values

训练模型

# Lasso(L1)正则回归

lasso = Lasso(alpha=1.0)

lasso.fit(X, y)

lasso_predict = lasso.predict(X)

# Ridge(L2)正则回归

ridge = Ridge(alpha=1.0)

ridge.fit(X, y)

ridge_predict = ridge.predict(X)

# ElasticNet(弹性网络)回归

# l1_ratio=0时等同于Lasso(L1)回归

elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)

elastic_net.fit(X, y)

elastic_net_predict = elastic_net.predict(X)

可视化

plt.scatter(X, y, c='gray', edgecolor='white', marker='s', label='训练数据')

plt.plot(X, lasso_predict, c='r',label='L1正则化',linestyle='--')

plt.plot(X, ridge_predict, c='b',label='L2正则化',linestyle='-')

plt.plot(X, elastic_net_predict, c='g',label='弹性网络',linestyle=':')

plt.xlabel('平均房间数目[MEDV]', fontproperties=font)

plt.ylabel('以1000美元为计价单位的房价[RM]', fontproperties=font)

plt.title('波士顿房价预测', fontproperties=font, fontsize=20)

plt.legend(prop=font)

plt.show()