02-10 正則化線性迴歸(波士頓房價預測)

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正則化線性迴歸(波士頓房價預測)

1、導入模塊

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')

2、獲取數據

df = pd.read_csv('housing-data.txt', sep='\s+', header=0)
X = df[['RM']].values
y = df['MEDV'].values

3、訓練模型

# Lasso(L1)正則迴歸
lasso = Lasso(alpha=1.0)
lasso.fit(X, y)
lasso_predict = lasso.predict(X)

# Ridge(L2)正則迴歸
ridge = Ridge(alpha=1.0)
ridge.fit(X, y)
ridge_predict = ridge.predict(X)

# ElasticNet(彈性網絡)迴歸
# l1_ratio=0時等同於Lasso(L1)迴歸
elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
elastic_net.fit(X, y)
elastic_net_predict = elastic_net.predict(X)

4、可視化

plt.scatter(X, y, c='gray', edgecolor='white', marker='s', label='訓練數據')
plt.plot(X, lasso_predict, c='r',label='L1正則化',linestyle='--')
plt.plot(X, ridge_predict, c='b',label='L2正則化',linestyle='-')
plt.plot(X, elastic_net_predict, c='g',label='彈性網絡',linestyle=':')
plt.xlabel('平均房間數目[MEDV]', fontproperties=font)
plt.ylabel('以1000美圓爲計價單位的房價[RM]', fontproperties=font)
plt.title('波士頓房價預測', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()

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