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An Illustrated Guide to the Poisson Regression Model
An Illustrated Guide to the Poisson Regression Model and a tutorial on Poisson regression using Python
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Poisson Regression and Generalised Linear Models
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…
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The Poisson distribution: From basic probability theory to regression models
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...
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A Gentle Introduction to Poisson Regression for Count Data: School’s Out, Job Offers Incoming!
Introduction Hey data enthusiasts! Today, we’re diving into the fascinating world of count data and its trusty sidekick, Poisson regression. Buckle up, because we’re about to explore how this statisti...
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Poisson Regression in R
Statistics in R Series Continue reading on Towards Data Science
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Generalized Poisson Regression for Real World Datasets
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...
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Predicting the Unpredictable: An Introduction to the Poisson Distribution
An overview of one of the most famous probability distributions Continue reading on Towards Data Science
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Poisson Regression Models for Time Series Data Sets
Learn how to build Poisson and Poisson-like models for time series data sets of counts based data, using Python and Statsmodels
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The Poisson Distribution
Mylie has been averaging 3 hits for every 10 times at bat. What is the probability that she will get exactly 2 hits in her next 5 times at bat? How do we go about calculating the probability of this…
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Exploring the Poisson Distribution
I find probability distributions would often be useful tools to know and understand, but the explanations are not always very intuitive. The Poisson distribution is one of the probability…
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Tutorial: Poisson regression with CatBoost
More specifically in our tutorial, we will focus on count-based data where each point is the number of events that have occurred over some fixed time unit t. We will also assume that on average…
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Linear Regression versus Poisson Regression
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...
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Poisson regression and non-normal loss
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...
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Adjust for Overdispersion in Poisson Regression
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…
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Essential Math for Data Science: The Poisson Distribution
The Poisson distribution, named after the French mathematician Denis Simon Poisson, is a discrete distribution function describing the probability that an event will occur a certain number of times…
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Poisson Distribution Details with Implementation
Part 6: Statistics Series Hey, welcome back to the statistics series. In the earlier article, we studied binomial distribution. https://medium.com/python-in-plain-english/important-points-about-binomi...
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An Introduction to the Poisson Integer ARIMA Regression model
Integer ARIMA models are used for modeling time series data consisting of whole numbered counts. The Poisson Integer ARIMA model bridges the gap between the ARIMA model for time series data and Poisso...
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The Modeling Limitations of Linear Regression Which Poisson Regression Addresses
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...
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You Can Build Any Linear Model If You Learn Just One Thing About Them
Many of you appreciated yesterday’s post on Poisson regression. Today, we shall continue discussing that topic, and I will help you cultivate what I think is one of the MOST overlooked and underapprec...
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Maximum Likelihood Estimation and Poisson Regression in the gravity model
In my previous article I introduced the basic concepts of probability and how this relates to maximum likelihood estimation and ordinary least squares regression. In this article, I will continue to…
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Shark Attack — explaining the use of Poisson regression
Nothing quite racks havoc on an idealistic summer swim off the Florida Keys quite like a shark attack. Images of an imposing dorsal fin, large “white” teeth and generalized panic aside, how much have…...
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Poisson Processes
This chapter introduces the Poisson process , which is a model used to describe events that occur at random intervals. As an example of a Poisson process, we’ll model goal-scoring in soccer, which is...
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The Most Underrated Skill in Training Linear Models
Yesterday’s post on Poisson regression was appreciated by many of you. Today, I want to build on that and help you cultivate what I think is one of the MOST overlooked and underappreciated skills in d...
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Poisson Regression: The Robust Extension of Linear Regression
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...
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