1.匯入模組

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

import matplotlib.pyplot as plt
%matplotlib inline

import sklearn.datasets as datasets

#機器演算法模型
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import
 
 Ridge
from sklearn.linear_model import Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR

#切割訓練資料和樣本資料
from sklearn.model_selection import train_test_split

#用於模型評分
from sklearn.metrics import r2_score

2.生成訓練資料和測試資料

boston = datasets.load_boston()
train = boston.data
target = boston.target

#切割資料樣本集合測試集
 

X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2)

3.建立學習模型

knn = KNeighborsRegressor()
linear = LinearRegression()
ridge = Ridge()
lasso = Lasso()
decision = DecisionTreeRegressor()
svr = SVR()

4.訓練模型

knn.fit(X_train,y_train)
linear.fit(X_train,y_train)
ridge.fit(X_train,y_train)
lasso.fit(X_train,y_train)
decision.fit(X_train,y_train)
svr.fit(X_train,y_train)
 

5.預測資料

y_pre_knn = knn.predict(x_test)
y_pre_linear = linear.predict(x_test)
y_pre_ridge = ridge.predict(x_test)
y_pre_lasso = lasso.predict(x_test)
y_pre_decision = decision.predict(x_test)
y_pre_svr = svr.predict(x_test)

6.評分

knn_score = r2_score(y_true,y_pre_knn)
linear_score=r2_score(y_true,y_pre_linear)
ridge_score=r2_score(y_true,y_pre_ridge)
lasso_score=r2_score(y_true,y_pre_lasso)
decision_score=r2_score(y_true,y_pre_decision)
svr_score=r2_score(y_true,y_pre_svr)
display(knn_score,linear_score,ridge_score,lasso_score,decision_score,svr_score)

7.繪圖

#KNN
plt.plot(y_true,label='true')
plt.plot(y_pre_knn,label='knn')
plt.legend()

#Linear
plt.plot(y_true,label='true')
plt.plot(y_pre_linear,label='linear')
plt.legend()

#Ridge
plt.plot(y_true,label='true')
plt.plot(y_pre_ridge,label='ridge')
plt.legend()

#lasso
plt.plot(y_true,label='true')
plt.plot(y_pre_lasso,label='lasso')
plt.legend()

#decision
plt.plot(y_true,label='true')
plt.plot(y_pre_decision,label='decision')
plt.legend()

#SVR
plt.plot(y_true,label='true')
plt.plot(y_pre_svr,label='svr')
plt.legend()