Regularizing models with Twitter data

You will work with the Twitter data expressing customers' sentiment about airline companies. The X matrix of features and y vector of labels have been created for you. In addition, the training and testing split has been performed. You can work with the X_train, X_test, y_train and y_test arrays directly.

You will train regularized and a more flexible models and evaluate them using different model performance metrics.

All required packages have been imported for you.

This exercise is part of the course

Sentiment Analysis in Python

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Exercise instructions

Train two logistic regressions: one with regularization parameter of 100 and a second of 0.1.
Print the accuracy scores of both models.
Print the confusion matrix of each model.

Hands-on interactive exercise

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

# Build a logistic regression with regularizarion parameter of 100
log_reg1 = ____.____
# Build a logistic regression with regularizarion parameter of 0.1
log_reg2 = ____.____

# Predict the labels for each model
y_predict1 = log_reg1.predict(X_test)
y_predict2 = log_reg2.predict(X_test)

# Print performance metrics for each model
print('Accuracy of model 1: ', ____(____, ____))
print('Accuracy of model 2: ', ____(___, ____))
print('Confusion matrix of model 1: \n' , ____(____, ____)/len(y_test))
print('Confusion matrix of model 2: \n', ____(____, ____)/len(y_test))

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