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Is logistic regression better than naive Bayes?
Naive Bayes also assumes that the features are conditionally independent. Real data sets are never perfectly independent but they can be close. In short Naive Bayes has a higher bias but lower variance compared to logistic regression. If the data set follows the bias then Naive Bayes will be a better classifier.
Why naive Bayes is best for sentiment analysis?
One common use of sentiment analysis is to figure out if a text expresses negative or positive feelings. Written reviews are great datasets for doing sentiment analysis because they often come with a score that can be used to train an algorithm. Naive Bayes is a popular algorithm for classifying text.
Which is a better classifier naive Bayes or logistic regression?
If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.
What’s the accuracy of the naive Bayes algorithm?
On testing the model on the test data set we get an accuracy of 99.4%. which is slightly less may be due to the assumptions that the Naive Bayes algorithm makes. In fact, it called “Naive” due to its assumptions.
Can you combine multiple n-gram sentiment models?
Combining multiple n-gram models, however, can quickly increase model complexity. The training of sentiment classifiers traditionally takes place in a supervised learning process. For this purpose, a training data set is used, which contains text sections with associated sentiment tendencies as prediction labels.
When to use lasso or ridge logistic regression?
Logistic regression: When the training data size is small relative to the number of features, including regularisation such as Lasso and Ridge regression can help reduce overfitting and result in a more generalised model.