# probability

## Underestimating risk

When I hear that a system has a one in a trillion (1,000,000,000,000) chance of failure, I immediately translate that…

## vikashraj luhaniwal

Recommending news articles based on already read articlesContent based recommendation in Python from…Why Probability distribution is must in DS/ML —As the name suggests…

## A Gentle Introduction to Probability Metrics for Imbalanced Classification

Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of…

## Random sample overlap

To make the problem slightly more general, take two samples of size √n from a population of size n where…

## Develop an Intuition for Bayes Theorem With Worked Examples

Last Updated on December 9, 2019Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively…

## How to Use an Empirical Distribution Function in Python

An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not…

## A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically,…

## A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There…

## A Gentle Introduction to Cross-Entropy for Machine Learning

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…

## How to Calculate the Divergence Between Probability Distributions

Last Updated on October 18, 2019 It is often desirable to quantify the difference between probability distributions for a given…

## How to Develop a Naive Bayes Classifier from Scratch in Python

Last Updated on October 7, 2019 Classification is a predictive modeling problem that involves assigning a label to a given…

## A Gentle Introduction to Bayes Theorem for Machine Learning

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can…

## Probability for Machine Learning (7-Day Mini-Course)

This is called the “Boy or Girl Problem” and is one of many common toy problems for practicing probability. Post…

## How to Develop an Intuition for Probability With Worked Examples

Probability calculations are frustratingly unintuitive. Our brains are too eager to take shortcuts and get the wrong answer, instead of…

## How to Develop an Intuition for Joint, Marginal, and Conditional Probability

Probability for a single random variable is straight forward, although it can become complicated when considering two or more variables.…

## A Gentle Introduction to Joint, Marginal, and Conditional Probability

Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the…

## 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…

## 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…

## 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…

## 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…

## What Is Probability?

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty…

## 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,…

## 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…