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 儲蓄保險王 · 2022-12-26

from sklearn.tree import DecisionTreeClassifier

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:

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

後半部類似的語法,

主要改用 DecisionTreeClassifier

輸出結果:

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:

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)

輸出結果:

criterion = “gini” 

 

criterion = “entropy” 

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儲蓄保險王

儲蓄險是板主最喜愛的儲蓄工具,最喜愛的投資理財工具則是ETF,最喜愛的省錢工具則是信用卡

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