Probability Density and Mass Functions in Machine Learning - Machine Philosopher

You will hear the term probability distribution many times when working with data and machine learning models. These are extremely helpful in certain cases such as naive Bayes' where the model needs to know a lot about the probabilities of its data! What it will be referring to is either the probability density function or the probability mass function of our data, lets have a look at the important differences! In machine learning, we often provide models with distributions of probabilities to tell us about what values any new data samples are likely to be. If we are working with continuous random variables, then we would use a probability density function to model the probability of any variable being near a certain value (continuous data does not have exact probabilities, as we will see below). When we are working with continuous data however, we note that each sample can be any value within a range.