Beslisboom voor regressie

Machine Learning met boomgebaseerde modellen in Python

Elie Kawerk

Data Scientist

Auto-mpg-dataset

auto

Auto-mpg met één feature

auto1D

Regressieboom in scikit-learn

# 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)

Regressieboom in scikit-learn

# 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

Informatiecriterium voor regressieboom

impurity-reg

Voorspelling

pred-reg

Lineaire regressie vs. regressieboom

auto1D

Laten we oefenen!

Machine Learning met boomgebaseerde modellen in Python