Machine Learning Algorithms

Multinomial naive Bayes

A multinomial distribution is useful to model feature vectors where each value represents, for example, the number of occurrences of a term or its relative frequency. If the feature vectors have n elements and each of them can assume k different values with probability p_k, then:

The conditional probabilities P(x_i|y) are computed with a frequency count (which corresponds to applying a maximum likelihood approach), but in this case, it's important to consider the alpha parameter (called Laplace smoothing factor). Its default value is 1.0 and it prevents the model from setting null probabilities when the frequency is zero. ...