Poisson Regression - Learn Data Science with Travis

Poisson-Regression

Poisson regression is a statistical technique used to model count data, particularly when the data represents the number of events occurring within a fixed interval of time or space. It is based on the Poisson distribution, which assumes that the mean and variance of the count data are equal. This method is particularly useful for analyzing rare events, such as the number of accidents at a traffic intersection or the incidence of a specific disease. Poisson regression allows researchers to understand the relationship between the count of events and one or more predictor variables, making it a valuable tool in various fields, including epidemiology and insurance.

Adjust for Overdispersion in Poisson Regression

Towards Data Science

The Poisson regression model naturally arises when we want to model the average number of occurrences per unit of time or space. For example, the incidence of rare cancer, the number of car crossing…

Poisson regression and non-normal loss

Scikit-learn Examples

Poisson regression and non-normal loss This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from 1 and compares it with a linear m...

Generalized Poisson Regression for Real World Datasets

Towards Data Science

Generalized Poisson Regression models are useful for modeling counts based data sets that are either under-dispersed or over-dispersed i.e. they do not obey the variance equal to mean requirement impo...

An Illustrated Guide to the Poisson Regression Model

Towards Data Science

An Illustrated Guide to the Poisson Regression Model and a tutorial on Poisson regression using Python

Poisson Regression and Generalised Linear Models

Towards Data Science

Linear Regression is the first algorithm most Data Scientists begin their journey with. It is an intiuative and easily implemented and visualised model for continous data. The second most learned…

Tweedie regression, or Poisson-Gamma regressions ?

R-bloggers

Yesterday, I was chating with a young and enthousiastic actuary, who asked a nice (and classical) question: is it the same, or not to use a Tweedie regression, or two regressions (Poisson, and Gamma)....

Poisson Regression Models for Time Series Data Sets

Towards Data Science

Learn how to build Poisson and Poisson-like models for time series data sets of counts based data, using Python and Statsmodels

Linear Regression versus Poisson Regression

Python in Plain English

Machine Learning and Linear Regression Perhaps the first machine learning algorithm that we all learn, the linear regression algorithm is surely one of the most fundamental and heavily used techniques...

The Poisson distribution: From basic probability theory to regression models

R-bloggers

Brief introduction to the Poisson distribution for modeling count data using the distributions3 package. The distribution is illustrated using the number of goals scored at the 2018 FIFA World Cup, su...

The Poisson Hidden Markov Model for Time Series Regression

Towards Data Science

A Poisson Hidden Markov Model uses a mixture of two random processes, a Poisson process and a discrete Markov process, to represent whole numbered time series data.

Poisson Regression in R

Towards Data Science

Regression is a vast world. We can do several types of regression analysis depending on the data type. We have covered logistic regression in detail in the previous articles. In this article, I will…

Poisson Regression: The Robust Extension of Linear Regression

Daily Dose of Data Science

Linear regression comes with its own set of challenges/assumptions. For instance: After modeling, the output can be negative for some inputs. But this may not make sense at times — predicting the numb...