回帰の決定木

Pythonで学ぶ木ベースのMachine Learning

Elie Kawerk

Data Scientist

Auto-mpg データセット

auto

Auto-mpg（特徴量1つ）

auto1D

scikit-learn での回帰木

# Import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
# Import train_test_split 
from sklearn.model_selection import train_test_split
# Import mean_squared_error as MSE
from sklearn.metrics import mean_squared_error as MSE

# Split data into 80% train and 20% test
X_train, X_test, y_train, y_test= train_test_split(X, y, 
                                                   test_size=0.2, 
                                                   random_state=3)

# Instantiate a DecisionTreeRegressor 'dt'
dt = DecisionTreeRegressor(max_depth=4, 
                           min_samples_leaf=0.1,
                           random_state=3)

scikit-learn での回帰木

# Fit 'dt' to the training-set
dt.fit(X_train, y_train)
# Predict test-set labels
y_pred = dt.predict(X_test)

# Compute test-set MSE
mse_dt =  MSE(y_test, y_pred)

# Compute test-set RMSE 
rmse_dt = mse_dt**(1/2)

# Print rmse_dt
print(rmse_dt)

5.1023068889

回帰木の情報基準

不純度-回帰

予測

予測-回帰

線形回帰 vs. 回帰木

auto1D

練習しましょう！

Pythonで学ぶ木ベースのMachine Learning