What is alpha parameter in multinomial Naive Bayes?

What is alpha parameter in multinomial Naive Bayes?

In Multinomial Naive Bayes, the alpha parameter is what is known as a hyperparameter; i.e. a parameter that controls the form of the model itself.

What is Alpha in Naive Bayes?

Most of the time, alpha = 1 is being used to resolve the problem of zero probability in the Naive Bayes algorithm. NOTE: Sometimes Laplace smoothing technique is also known as “Add one smoothing”. In Laplace smoothing, 1 (one) is added to all the counts, and thereafter, the probability is calculated.

Why do you think that Naive Bayes is a good choice when it comes to text classification problems such as spam detection?

When you actually get to work with the above algorithms, Naive Bayes gives you the best kind of results which are desired. In applications like spam filtering and sentiment analysis, the data majorly consists of the textual data in the form of reviews or the contents of an email.

Why do we need smoothing in naive Bayes?

Laplace smoothing is a smoothing technique that helps tackle the problem of zero probability in the Naïve Bayes machine learning algorithm. Using higher alpha values will push the likelihood towards a value of 0.5, i.e., the probability of a word equal to 0.5 for both the positive and negative reviews.

Why do we need smoothing in Naive Bayes?

How does the mutlinomial Bayes’s alpha parameter affect the text?

The basic idea is to increase the probablities of all bigrams in your non-maximum likelihood equation (Since we are changing the counts from what they occurred in hopes to make it better) by 1 to make everything non-zero. That increase by 1 is called adding the pseudocount or as you know it as α.

How is multinomial naive Bayes used in text classification?

Naïve Bayes, which is computationally very efficient and easy to implement, is a learning algorithm frequently used in text classification problems. Two event models are commonly used: The Multivariate Event model is referred to as Multinomial Naive Bayes.

Is the alpha parameter a hyperparameter or a parameter?

In Multinomial Naive Bayes, the alpha parameter is what is known as a hyperparameter; i.e. a parameter that controls the form of the model itself.

How to calculate naive Bayes parameter alpha in Python?

For classifying query point in NB P (Y=1|W) or P (Y=0|W) (considering binary classification) here W is vector of words W= [w1, w2, w3…. wd] d = number of features Same above should be done for Y=0. For Naive Bayes formula refer this ( https://en.wikipedia.org/wiki/Naive_Bayes_classifier)