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Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = “gini”) #criterion = “entropy” #criterion: 標準，準則

by · 2022-12-26

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Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

from sklearn.tree import DecisionTreeClassifier

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

前篇使用了

K-近鄰演算法(K Nearest Neighbor ,簡稱 KNN)

本篇要改用決策樹分類

處理的資料: https://pse.is/3ty6rk

部分資料:

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
folder = “C:\Python\P107\doc”
fname = “student.csv”
import os
fpath = os.path.join(folder,fname)
# fpath = folder + “\\” + fname #同義
df = pd.read_csv(fpath,header=None,skiprows=[0])
df.to_excel(os.path.join(folder,”knsDF.xlsx”))
#df.index.size = 40  #df.columns.size = 5
X = df.drop([4],axis=1).values
y = df[4].values
Xtrain, Xtest, ytrain, ytest =\
    train_test_split(X,y,test_size=0.25,
                     random_state= 42 ,shuffle =True)
XtestDF = pd.DataFrame(Xtest)    
XtestDF.to_excel(os.path.join(folder,”knsXtest.xlsx”))
print(“Xtrain.shape:”,Xtrain.shape) #(30, 3)
print(“ytrain.shape:”,ytrain.shape) #(30,)
print(“Xtest.shape:”,Xtest.shape) #(10, 3)
print(“ytest.shape:”,ytest.shape) #(10,)

from sklearn.tree import DecisionTreeClassifier
for cri in [“gini”,”entropy”]:
    tree = DecisionTreeClassifier(criterion = cri)
    tree.fit(Xtrain,ytrain)
    pred  = tree.predict(Xtest)
    pred2 = tree.predict_proba(Xtest)
    print(“prediction:”,pred,”in case criterion”,cri )
    print(“prediction proability:”,pred2,
          “\nin case criterion”,cri )
    howgood = tree.score(Xtest,ytest)
    print(“Goodness:”,howgood,”in case ctrterion”,cri)
#前半部同KNN

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

後半部類似的語法,

主要改用 DecisionTreeClassifier

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

輸出結果:

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

就算有第0欄(index,非資料)干擾

決策樹的score仍比

K-近鄰演算法(KNN)去掉第0欄高

 

匯出決策樹:

Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

改寫後半段程式碼:
 
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text
for cri in [“gini”,”entropy”]:
    tree = DecisionTreeClassifier(criterion = cri)
    tree.fit(Xtrain,ytrain)
    r = export_text(tree,feature_names =
                    [“item”,”English”,”Math”,”Chinese”],
                    show_weights=True)
    print(r)
Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王
輸出結果:
criterion = “gini” 
Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王
 
criterion = “entropy” 
Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

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

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Python機器學習: 決策樹 (DecisionTreeClassifier) ; from sklearn.tree import DecisionTreeClassifier ; tree = DecisionTreeClassifier(criterion = "gini") #criterion = “entropy” #criterion: 標準，準則 - 儲蓄保險王

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