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()
