sklearn學習筆記

決策樹一般採用整合，具有隨機，不純度最優

from sklearn import tree

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split#用於劃分訓練測試集數目
wine=load_wine()
x_train,x_test,y_train,y_test=train_test_split(wine.data,wine.target,test_size=0.3)
#help(train_test_split)

#bulid model

clf=tree.decisiontreeclassifier( criterion="entropy"
,random_state=30)#隨機數，決策樹是隨機的，設定了該引數，則模型不變
#help(tree.decisiontreeclassifier)
clf = clf.fit(x_train, y_train)
score=clf.score(x_test,y_test)
clf.predict(x_test)#返回標籤

#視覺化

import pandas as pd
pd.concat([pd.dataframe(wine.data),pd.dataframe(wine.target)],axis=1)
import graphviz
dot_data = tree.export_graphviz(clf
,feature_names=wine.feature_names
,class_names=wine.target_names
,filled=true
,rounded=true
, out_file=none)
#help(tree.export_graphviz)
graph = graphviz.source(dot_data)
graph.render("iris")
clf.feature_importances_
[*zip(wine.feature_names,clf.feature_importances_)]

clf=tree.decisiontreeclassifier( criterion="entropy"

,random_state=30
,splitter='random')

import matplotlib.pyplot as plt

test=
for i in range (10):
clf=tree.decisiontreeclassifier( criterion="entropy"
,random_state=30
,splitter='random'
,max_depth=i+1
#,min_samples_leaf =10
#,min_impurity_decrease=3)
)clf = clf.fit(x_train, y_train)
#score1=clf.score(x_train, y_train)
score=clf.score(x_test,y_test)
plt.plot(range(1,11),test,color='red',label='max_depth')
plt.show()

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