dataaspirant-regularization-ridge-regression.py

## Ridge regression

## two lists to hold alpha values and cross-validation scores
alpha = []
ridge_scores = []

## loop over different alpha values
for i in range(1,10):
    ridge_model = Ridge(alpha=0.25*i)
    ridge_model.fit(X_train, y_train)
    scores = cross_val_score(ridge_model, X, y, cv=10)
    average_score = mean(scores)*100
    ridge_scores.append(average_score)
    alpha.append(0.25*i)

## printing the scores
for i in range(0, len(alpha)):
    print(str(alpha[i]) + ' -> ' + str(ridge_scores[i]))
    
"""
## Outputs
0.25 -> 69.12811921100793
0.5 -> 69.1282936467351
0.75 -> 69.12844835069451
1.0 -> 69.12858352574278
1.25 -> 69.12869937241088
1.5 -> 69.12879608893493
1.75 -> 69.12887387128701
2.0 -> 69.128932913205
2.25 -> 69.12897340622241
"""