11wk-41: Medical Cost / 의사결정나무 max_feature,random_state

1. 강의영상

2. Imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.tree
import graphviz
#---#
import warnings
warnings.filterwarnings('ignore')

3. 데이터준비

df_train = pd.read_csv('https://raw.githubusercontent.com/guebin/MP2023/main/posts/insurance.csv')
df_train

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520
...	...	...	...	...	...	...	...
1333	50	male	30.970	3	no	northwest	10600.54830
1334	18	female	31.920	0	no	northeast	2205.98080
1335	18	female	36.850	0	no	southeast	1629.83350
1336	21	female	25.800	0	no	southwest	2007.94500
1337	61	female	29.070	0	yes	northwest	29141.36030

1338 rows × 7 columns

4. max_features

- max_features에 대한 제한을 주지 않음 \(\to\) 항상 같은 결과가 나옴

# step1
X = pd.get_dummies(df_train.loc[:,'age':'region'],drop_first=True)
y = df_train['charges']
# step2 
predictr = sklearn.tree.DecisionTreeRegressor()
# step3 
predictr.fit(X,y)
# step4 -- pass 

DecisionTreeRegressor()

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sklearn.tree.plot_tree(predictr,max_depth=0,feature_names=X.columns);

- max_features=4로 제한

len(X.columns) 

8

• max_features=4로 제한한다는 의미는 8개의 설명변수중에서 4개만 임의로 뽑아서 그중에서 “최적의 변수”와 “최적의 \(c\)”를 찾겠다는 의미

# step1
X = pd.get_dummies(df_train.loc[:,'age':'region'],drop_first=True)
y = df_train['charges']
# step2 
predictr = sklearn.tree.DecisionTreeRegressor(max_features=4)
# step3 
predictr.fit(X,y)
# step4 -- pass 

DecisionTreeRegressor(max_features=4)

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sklearn.tree.plot_tree(predictr,max_depth=0,feature_names=X.columns);

• 절반정도는 smoking 유무가 가장 위에 위치한다.

5. random_state

- max_features=4로 제한

# step1
X = pd.get_dummies(df_train.loc[:,'age':'region'],drop_first=True)
y = df_train['charges']
# step2 
predictr = sklearn.tree.DecisionTreeRegressor(max_features=4,random_state=43)
# step3 
predictr.fit(X,y)
# step4 -- pass 

DecisionTreeRegressor(max_features=4, random_state=43)

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sklearn.tree.plot_tree(predictr,max_depth=0,feature_names=X.columns);