This video lecture by Dr. Gabriele Durrant Creative Commons Attribution license (reuse allowed) introduces Poisson regression, a form of regression analysis...
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In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.
Deep learning is often used to make predictions for data driven analysis. But what are the meanings of these predictions? This post explains how neural networks used in deep learning provide the parameters of a statistical model describing the probability of the occurrence of events.
The Association of Data Scientists has announced a hands-on workshop on deep learning model deployment and management.
Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics.
Statistical-model fitting and analysis of results, visualization techniques, and prediction of the outcomes. This article will explain a statistical modeling technique with an example. I will explain a logistic regression modeling for binary outcome variables here.