CODEWITHSUNDEEP
pythonmachine-learningscikit-learncross-validationnaivebayes
Debbie
Debbie

Reputation: 969

Python + Scikit-learn:How to plot the curves of training score and validation score against the additive smoothing parameter alpha

I am using k-fold cross validation to compute the optimal value of Additive Smoothing parameter alpha. Also, I want to plot the curves of training accuracy and validation accuracy against the values of alpha. I wrote a code for that:

alphas = list(np.arange(0.0001, 1.5000, 0.0001))

#empty lists that stores cv scores and training_scores
cv_scores = []
training_scores = []

#perform k fold cross validation
for alpha in alphas:
    naive_bayes = MultinomialNB(alpha=alpha)
    scores = cross_val_score(naive_bayes, x_train_counts, y_train, cv=20, scoring='accuracy')
    scores_training = naive_bayes.fit(x_train_counts, y_train).score(x_train_counts, y_train)

    cv_scores.append(scores.mean())
    training_scores.append(scores_training)

#plot cross-validated score, training score vs alpha 

plt.plot(alphas, cv_scores, 'r')
plt.plot(alphas, training_scores, 'b')
plt.xlabel('alpha')
plt.ylabel('score')

Is this the correct way to implement this?

Upvotes: 0

Views: 1362

Answers (1)

nagrom97
nagrom97

Reputation: 514

Depending on whether you want to tweak other model hyper parameters it may be easier to use what is called a grid search. Using this, you can tweak extra hyper parameters in a simpler way and training scores are available for you. See my below implementation.

parameters = {'alpha':[0.0001, 1.5000, 0.0001]}
classifier = GridSearchCV(MultinomialNB(), parameters, cv=20)
clf.fit(x_train, y_train)

print('Mean train set score: {}'.format(clf.cv_results_['mean_train_score']))

Upvotes: 1

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