Build and assess a model: product reviews data
In this exercise, you will build a logistic regression using the reviews dataset, containing customers' reviews of Amazon products. The array y contains the sentiment : 1 if positive and 0 otherwise. The array X contains all numeric features created using a BOW approach. Feel free to explore them in the IPython Shell.
Your task is to build a logistic regression model and calculate the accuracy and confusion matrix using the test dataset.
The logistic regression and train/test splitting functions have been imported for you.
Bài tập này là một phần của khóa học
Sentiment Analysis in Python
Hướng dẫn bài tập
- Import the accuracy score and confusion matrix functions.
- Split the data into training and testing, using 30% of it as a test set and set the random seed to
42. - Train a logistic regression model.
- Print out the accuracy score and confusion matrix using the test data.
Bài tập tương tác thực hành trực tiếp
Hãy thử làm bài tập này bằng cách hoàn thành đoạn mã mẫu này.
# Import the accuracy and confusion matrix
____
# Split the data into training and testing
X_train, X_test, y_train, y_test = ____(____, ____, ____=0.3, ____=42)
# Build a logistic regression
log_reg = ____._____
# Predict the labels
y_predict = log_reg.predict(X_test)
# Print the performance metrics
print('Accuracy score of test data: ', ____(____, ____))
print('Confusion matrix of test data: \n', ____(____, ____)/len(y_test))