Product reviews with regularization
In this exercise, you will work once more with the reviews dataset of Amazon product reviews. A vector of labels y contains the sentiment : 1 if positive and 0 otherwise. The matrix X contains all numeric features created using a BOW approach.
You will need to train two logistic regression models with different levels of regularization and compare how they perform on the test data. Remember that regularization is a way to control the complexity of the model. The more regularized a model is, the less flexible it is but the better it can generalize. Models with higher level of regularization are often less accurate than non-regularized ones.
Bài tập này là một phần của khóa học
Sentiment Analysis in Python
Hướng dẫn bài tập
- Split the data into a train and test sets.
- Train a logistic regression with regularization parameter of
1000. Train a second logistic regression with regularization parameter equal to0.001. - Print the accuracy scores of both models on the test set.
Bài tập tương tác thực hành trực tiếp
Hãy thử làm bài tập này bằng cách hoàn thành đoạn mã mẫu này.
# Split data into training and testing
____, ____, ____, ____ = train_test_split(____, ____, test_size=0.2, random_state=123)
# Train a logistic regression with regularization of 1000
log_reg1 = ____(____=1000).fit(X_train, y_train)
# Train a logistic regression with regularization of 0.001
log_reg2 = ____(____=0.001).fit(X_train, y_train)
# Print the accuracies
print('Accuracy of model 1: ', log_reg1.____(____, ____))
print('Accuracy of model 2: ', log_reg2.____(____, ____))