Improving your model

Your job in this exercise is to test a few different alpha levels using the Tfidf vectors to determine if there is a better performing combination.

The training and test sets have been created, and tfidf_vectorizer, tfidf_train, and tfidf_test have been computed.

Create a list of alphas to try using np.arange(). Values should range from 0 to 1 with steps of 0.1.
Create a function train_and_predict() that takes in one argument: alpha. The function should:
- Instantiate a MultinomialNB classifier with alpha=alpha.
- Fit it to the training data.
- Compute predictions on the test data.
- Compute and return the accuracy score.
Using a for loop, print the alpha, score and a newline in between. Use your train_and_predict() function to compute the score. Does the score change along with the alpha? What is the best alpha?