“Algoritma regresi sampel menggunakan pipa dengan tuning hiperparameter” Kode Jawaban

Algoritma regresi sampel menggunakan pipa dengan tuning hiperparameter

# Setup the pipeline steps: steps
steps = [('imputation', Imputer(missing_values='NaN', strategy='mean', axis=0)),
         ('scaler', StandardScaler()),
         ('elasticnet', ElasticNet())]
         
# Create the pipeline: pipeline
pipeline = Pipeline(steps)

# Specify the hyperparameter space
parameters = {'elasticnet__l1_ratio':np.linspace(0,1,30)}
…
# Compute and print the metrics
r2 = gm_cv.score(X_test, y_test)
print("Tuned ElasticNet Alpha: {}".format(gm_cv.best_params_))
print("Tuned ElasticNet R squared: {}".format(r2))