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.

This exercise is part of the course

Introduction to Natural Language Processing in Python

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Exercise instructions

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?

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the list of alphas: alphas
alphas = ____

# Define train_and_predict()
def ____(____):
    # Instantiate the classifier: nb_classifier
    nb_classifier = ____
    # Fit to the training data
    ____
    # Predict the labels: pred
    pred = ____
    # Compute accuracy: score
    score = ____
    return score

# Iterate over the alphas and print the corresponding score
for alpha in alphas:
    print('Alpha: ', alpha)
    print('Score: ', ____)
    print()

Edit and Run Code