Discrete Probability Distributions for Machine Learning

Last Updated on February 10, 2020

The probability for a discrete random variable can be summarized with a discrete probability distribution.

Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing.

Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function.

Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about.

In this tutorial, you will discover discrete probability distributions used in machine learning.

After completing this tutorial, you will know:

• The probability of outcomes for discrete random variables can be summarized using discrete probability distributions.
• A single binary outcome has a Bernoulli distribution, and a sequence of binary outcomes has a Binomial distribution.
• A single categorical outcome has a Multinoulli distribution, and a sequence of categorical outcomes has a
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