Adjusting the regularization strength
Your current Lasso model has an \(R^2\) score of 84.7%. When a model applies overly powerful regularization it can suffer from high bias, hurting its predictive power.
Let's improve the balance between predictive power and model simplicity by tweaking the alpha parameter.
Questo esercizio fa parte del corso
Dimensionality Reduction in Python
Istruzioni dell'esercizio
- Find the highest value for
alphathat gives an \(R^2\) value above 98% from the options:1,0.5,0.1, and0.01.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Find the highest alpha value with R-squared above 98%
la = Lasso(____, random_state=0)
# Fits the model and calculates performance stats
la.fit(X_train_std, y_train)
r_squared = la.score(X_test_std, y_test)
n_ignored_features = sum(la.coef_ == 0)
# Print peformance stats
print(f"The model can predict {r_squared:.1%} of the variance in the test set.")
print(f"{n_ignored_features} out of {len(la.coef_)} features were ignored.")