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

Continue Reading# probability

## 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…

Continue Reading## 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…

Continue Reading## Random sample overlap

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

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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,…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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.…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## What Is Probability?

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

Continue Reading## 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,…

Continue Reading## 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…

Continue Reading## The information paradox

The information paradoxAndrea BerdondiniBlockedUnblockFollowFollowingJul 9ABSTRACT: The following paradox is based on the consideration that the value of a statistical datum…

Continue Reading## WHAT and WHY of Log Odds

WHAT and WHY of Log OddsPiyush AgarwalBlockedUnblockFollowFollowingJul 8The three main categories of Data Science are Statistics, Machine Learning and Software Engineering.…

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