决策树分裂过程可视化解析-优快云博客

本文链接：https://blog.youkuaiyun.com/fang156239305/article/details/119778690

本文深入探讨了决策树的学习过程，重点在于如何可视化展示决策树的节点分裂。通过实例，详细解释了如何利用可视化工具揭示决策树在特征选择和分割点确定上的策略，帮助读者更好地理解决策树算法的工作原理及其内在决策逻辑。

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# -*- coding: utf-8 -*-
"""
Created on Mon Aug 16 10:43:40 2021

@author: 1
"""
import dtreeviz
import pandas as pd
import numpy as np
from sklearn.datasets import *
from sklearn import tree
'''
iris = load_iris()
df_iris = pd.DataFrame(iris['data'],columns = iris['feature_names'])
df_iris['target'] = iris['target']
'''
data = pd.read_csv('water_data.csv',index_col=0)
data['TN'] = data['TN'].apply(pd.to_numeric, errors='coerce')
data['TEMP'] = data['TEMP'].apply(pd.to_numeric, errors='coerce')
data['COND'] = data['COND'].apply(pd.to_numeric, errors='coerce')
data['TURB'] = data['TURB'].apply(pd.to_numeric, errors='coerce')

data_feature = data.drop(['lable'], axis=1)

feature_names=list(data)
feature_names.pop(9)
target_names=['0','1','2','3','4','5']
data['lable']=data['lable']-1



clf = tree.DecisionTreeClassifier()
clf.fit(data_feature,data['lable'])

import graphviz 
dot_data = tree.export_graphviz(clf, out_file=None, 
                     feature_names=feature_names,  
                     class_names=target_names,  
                     filled=True, rounded=True,  
                     special_characters=True)  

graph = graphviz.Source(dot_data)  
graph 

from dtreeviz.trees import dtreeviz
viz = dtreeviz(clf,
               data_feature,
               data['lable'],
               target_name='',
               feature_names=np.array(feature_names),
               class_names={0:'0',1:'1',2:'2',3:'3',4:'4',5:'5'},scale=3)


viz1 = dtreeviz(clf,
               data_feature,
               data['lable'],
               target_name='',
               feature_names=np.array(feature_names),
               class_names={0:'0',1:'1',2:'2',3:'3',4:'4',5:'5'},
               X=data_feature.loc[0])