2-1-1. Decision Tree 실습 1

사이킷런에서 제공하는 의사결정나무 코드를 실습한다.

 

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

clf.predict([[2., 2.]])

clf.predict_proba([[2., 2.]])

from sklearn.datasets import load_iris
from sklearn import tree
X, y = load_iris(return_X_y=True)
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)

tree.plot_tree(clf) 

import graphviz 
dot_data = tree.export_graphviz(clf, out_file=None) 
graph = graphviz.Source(dot_data) 
graph.render("iris") 

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

 

참조 : scikit-learn.org/stable/modules/tree.html#classification

1.10. Decision Trees — scikit-learn 0.23.2 documentation

https://scikit-learn.org/stable/index.html