本文實(shí)例為大家分享了python機(jī)器學(xué)習(xí)實(shí)現(xiàn)決策樹的具體代碼，供大家參考，具體內(nèi)容如下

# -*- coding: utf-8 -*-"""Created on Sat Nov 9 10:42:38 2019@author: asus""""""決策樹目的：1. 使用決策樹模型2. 了解決策樹模型的參數(shù)3. 初步了解調(diào)參數(shù)要求：基于乳腺癌數(shù)據(jù)集完成以下任務(wù)：1.調(diào)整參數(shù)criterion，使用不同算法信息熵（entropy）和基尼不純度算法(gini)2.調(diào)整max_depth參數(shù)值，查看不同的精度3.根據(jù)參數(shù)criterion和max_depth得出你初步的結(jié)論。"""import matplotlib.pyplot as pltimport numpy as npimport pandas as pdimport mglearn from sklearn.model_selection import train_test_split#導(dǎo)入乳腺癌數(shù)據(jù)集from sklearn.datasets import load_breast_cancerfrom sklearn.tree import DecisionTreeClassifier#決策樹并非深度越大越好，考慮過擬合的問題#mglearn.plots.plot_animal_tree()#mglearn.plots.plot_tree_progressive()#獲取數(shù)據(jù)集cancer = load_breast_cancer()#對(duì)數(shù)據(jù)集進(jìn)行切片X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target,       stratify = cancer.target,random_state = 42)#查看訓(xùn)練集和測(cè)試集數(shù)據(jù)      print('train dataset :{0} ;test dataset :{1}'.format(X_train.shape,X_test.shape))#建立模型(基尼不純度算法(gini)),使用不同最大深度和隨機(jī)狀態(tài)和不同的算法看模型評(píng)分tree = DecisionTreeClassifier(random_state = 0,criterion = 'gini',max_depth = 5)#訓(xùn)練模型tree.fit(X_train,y_train)#評(píng)估模型print("Accuracy(準(zhǔn)確性) on training set: {:.3f}".format(tree.score(X_train, y_train)))print("Accuracy(準(zhǔn)確性) on test set: {:.3f}".format(tree.score(X_test, y_test)))print(tree)# 參數(shù)選擇 max_depth,算法選擇基尼不純度算法(gini) or 信息熵（entropy）def Tree_score(depth = 3,criterion = 'entropy'): """ 參數(shù)為max_depth(默認(rèn)為3)和criterion(默認(rèn)為信息熵entropy)， 函數(shù)返回模型的訓(xùn)練精度和測(cè)試精度 """ tree = DecisionTreeClassifier(criterion = criterion,max_depth = depth) tree.fit(X_train,y_train) train_score = tree.score(X_train, y_train) test_score = tree.score(X_test, y_test) return (train_score,test_score)#gini算法，深度對(duì)模型精度的影響depths = range(2,25)#考慮到數(shù)據(jù)集有30個(gè)屬性scores = [Tree_score(d,'gini') for d in depths]train_scores = [s[0] for s in scores]test_scores = [s[1] for s in scores]plt.figure(figsize = (6,6),dpi = 144)plt.grid()plt.xlabel("max_depth of decision Tree")plt.ylabel("score")plt.title("'gini'")plt.plot(depths,train_scores,'.g-',label = 'training score')plt.plot(depths,test_scores,'.r--',label = 'testing score')plt.legend()#信息熵（entropy），深度對(duì)模型精度的影響scores = [Tree_score(d) for d in depths]train_scores = [s[0] for s in scores]test_scores = [s[1] for s in scores]plt.figure(figsize = (6,6),dpi = 144)plt.grid()plt.xlabel("max_depth of decision Tree")plt.ylabel("score")plt.title("'entropy'")plt.plot(depths,train_scores,'.g-',label = 'training score')plt.plot(depths,test_scores,'.r--',label = 'testing score')plt.legend()

運(yùn)行結(jié)果：