Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the probability for a single variable. Nevertheless, in machine learning, we often have many random variables that interact in often complex and unknown ways. There are specific techniques that can be used to quantify the probability […]

# Archive | Probability

## A Gentle Introduction to Probability Density Estimation

Probability density is the relationship between observations and their probability. Some outcomes of a random variable will have low probability density and other outcomes will have a high probability density. The overall shape of the probability density is referred to as a probability distribution, and the calculation of probabilities for specific outcomes of a random […]

## Continuous Probability Distributions for Machine Learning

The probability for a continuous random variable can be summarized with a continuous probability distribution. Continuous probability distributions are encountered in machine learning, most notably in the distribution of numerical input and output variables for models and in the distribution of errors made by models. Knowledge of the normal continuous probability distribution is also required […]

## Discrete Probability Distributions for Machine Learning

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 […]

## A Gentle Introduction to Probability Distributions

Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all possible outcomes. A thing of interest in probability is called a random variable, and the relationship between each possible outcome for a random variable and their probabilities is called a probability distribution. Probability distributions are […]

## What Is Probability?

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled […]

## A Gentle Introduction to Uncertainty in Machine Learning

Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any models developed from such data. Managing the uncertainty that is inherent in machine learning for predictive […]

## 5 Reasons to Learn Probability for Machine Learning

Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started. This is misleading advice, as probability makes more sense to a practitioner once they have the context of the applied machine […]

## Resources for Getting Started With Probability in Machine Learning

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem […]

## How to Develop and Evaluate Naive Classifier Strategies Using Probability

A Naive Classifier is a simple classification model that assumes little to nothing about the problem and the performance of which provides a baseline by which all other models evaluated on a dataset can be compared. There are different strategies that can be used for a naive classifier, and some are better than others, depending […]