Python機器學習: 線性迴歸, 計算均方誤差 (metrics.mean_squared_error), 判定係數 (metrics.r2_score), 皮爾森積差相關係數 (pearsonr) ; 以波士頓地區房價為例

MSE(mean_squared_error):

from sklearn import linear_model,metrics #指標

mse = metrics.mean_squared_error(ytest,ypredict)

判定係數(R-squared):

y^: 預測值

y bar: 平均值

計算判定係數(R-squared):

皮爾森積差相關係數

(Pearson correlation coefficient):

計算皮爾森積差相關係數:

多維度線性迴歸:

from sklearn import linear_model,metrics
import pandas as pd
import sys
fpath = r”C:\Python\P107\doc\BostonHousing.csv”
dataset = pd.read_csv(fpath) #.shape = (506, 14)
headerList = dataset.columns.tolist()
cols = dataset.columns.size
# dataset.columns.size = 14
# ==================================================
# x = dataset.drop(headerList[-1],axis=1).values #(506, 13)
# y = dataset[headerList[-1]].values #(506,)
# ==================================================
x=dataset.iloc[:,0:cols-1].values #(506, 13)
y=dataset.iloc[:,cols-1:].values.ravel() #(506,)
#沒有ravel()的話,(506,1) 多出一個維度,第二維長度僅有1
from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest =\
train_test_split(x,y,test_size=0.3,
random_state=42,shuffle=True)

# 1.  train_test_split()將原始資料切割為

#       xtrain, xtest, ytrain, ytest
# =============================================
# print(“The shpae of training data(X axis):”,xtrain.shape)
# print(“The shpae of training data(Y axis):”,ytrain.shape)
# print(“The shpae of testing data(X axis):”,xtest.shape)
# print(“The shpae of testing data(Y axis):”,ytest.shape)

# ==============================================

regr_train = linear_model.LinearRegression()

#2. 建立LinearRegression操作子

regr_train.fit(xtrain,ytrain)

#3. 用. fit(xtrain, ytrain) 影響操作子

ypredict = regr_train.predict(xtest)

#4. 用.predict(xtest) 生出預測值ypredict 

print(“The predition data(Y axis):”,ypredict)
print(“The shape of predition data(Y axis):”,ypredict.shape)
mse = metrics.mean_squared_error(ytest,ypredict)
r2score = metrics.r2_score(ytest,ypredict)

# 5. ytest是真實資料,ypredict是預測值,

#有這兩者就可以用來計算mse跟r2_score
print(“mean_squared_error metrics:”,mse)
print(“r-squared metrics:”,r2score)

輸出結果:

p value:

推薦hahow線上學習python: https://igrape.net/30afN