Python 데이터 분석
Python 데이터분석 기초 63 - DecisionTreeRegressor, RandomForestRegressor
코딩탕탕
2022. 11. 23. 10:09
중요변수 얻을 때는 RandomForestRegressor를 사용하는 것을 추천한다.
그 이외에는 ols를 추천
#
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston
from sklearn.metrics import r2_score
boston = load_boston()
# print(boston.keys())
dfx = pd.DataFrame(boston.data, columns = boston.feature_names)
dfy = pd.DataFrame(boston.target, columns = ['MEDV'])
df = pd.concat([dfx, dfy], axis = 1)
print(df.head(3))
print(df.corr())
# 시각화
cols = ['MEDV', 'RM', 'LSTAT']
# sns.pairplot(df[cols])
# plt.show()
# 단순선형회귀
x = df[['LSTAT']].values
y = df['MEDV'].values
print(x[:3])
print(y[:3])
print('DecisionTreeRegressor')
model = DecisionTreeRegressor(criterion = 'mse', random_state = 123).fit(x, y)
print('예측값 :', model.predict(x[:5]))
print('실제값 :', y[:5])
print('결정계수 :', r2_score(y, model.predict(x))) # 0.9590
print('RandomForestRegressor')
model2 = RandomForestRegressor(criterion = 'mse', n_estimators = 100, random_state = 123).fit(x, y)
print('예측값 :', model2.predict(x[:5]))
print('실제값 :', y[:5])
print('결정계수 :', r2_score(y, model2.predict(x))) # 0.9081
<console>
CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT MEDV
0 0.00632 18.0 2.31 0.0 0.538 ... 296.0 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0.0 0.469 ... 242.0 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0.0 0.469 ... 242.0 17.8 392.83 4.03 34.7
[3 rows x 14 columns]
CRIM ZN INDUS ... B LSTAT MEDV
CRIM 1.000000 -0.200469 0.406583 ... -0.385064 0.455621 -0.388305
ZN -0.200469 1.000000 -0.533828 ... 0.175520 -0.412995 0.360445
INDUS 0.406583 -0.533828 1.000000 ... -0.356977 0.603800 -0.483725
CHAS -0.055892 -0.042697 0.062938 ... 0.048788 -0.053929 0.175260
NOX 0.420972 -0.516604 0.763651 ... -0.380051 0.590879 -0.427321
RM -0.219247 0.311991 -0.391676 ... 0.128069 -0.613808 0.695360
AGE 0.352734 -0.569537 0.644779 ... -0.273534 0.602339 -0.376955
DIS -0.379670 0.664408 -0.708027 ... 0.291512 -0.496996 0.249929
RAD 0.625505 -0.311948 0.595129 ... -0.444413 0.488676 -0.381626
TAX 0.582764 -0.314563 0.720760 ... -0.441808 0.543993 -0.468536
PTRATIO 0.289946 -0.391679 0.383248 ... -0.177383 0.374044 -0.507787
B -0.385064 0.175520 -0.356977 ... 1.000000 -0.366087 0.333461
LSTAT 0.455621 -0.412995 0.603800 ... -0.366087 1.000000 -0.737663
MEDV -0.388305 0.360445 -0.483725 ... 0.333461 -0.737663 1.000000
[14 rows x 14 columns]
[[4.98]
[9.14]
[4.03]]
[24. 21.6 34.7]
DecisionTreeRegressor
예측값 : [24. 21.6 34.7 33.4 32.8]
실제값 : [24. 21.6 34.7 33.4 36.2]
결정계수 : 0.9590088126871839
RandomForestRegressor
예측값 : [24.469 21.975 35.48173333 38.808 32.11911667]
실제값 : [24. 21.6 34.7 33.4 36.2]
결정계수 : 0.9081654854048482
