지도학습 tree 분류기

#Decision Tree Review
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd

iris = load_iris()
#iris.target=pd.get_dummies(iris.target)
#iris.target=iris.target.idxmax(axis=1)
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=11)

# 학습
model = DecisionTreeClassifier(random_state=1).fit(X_train, y_train)
#random_state =1 로 해주면 각 분류 모형의 default 값으로 들어간다  
model.predict(test데이터셋.csv)

# 결과 시각화
import matplotlib.pyplot as plt
from sklearn import tree

plt.figure( figsize=(15,10) )
tree.plot_tree(model, 
               class_names=iris.target_names,
               feature_names=iris.feature_names,
               impurity=True, filled=True, rounded=True)

#가지치기(Pruning)
model = DecisionTreeClassifier(max_depth=2, random_state=1)
model.fit(X_train, y_train)

max_depth 설정해주면 그 이후의 depth로 나무가 만들어지지 않음