In the rest of the section, we’ll learn about the NB model and see how to use it on the bicyclist counts data set. The Negative Binomial (NB) regression model is one such model that does not make the variance = mean assumption about the data. In such cases, one needs to use a regression model that will not make the equi-dispersion assumptioni.e.not assume that variance=mean. Often, the variance is greater than the mean, a property called over-dispersion, and sometimes the variance is less than the mean, called under-dispersion. This rather strict criterion is often not satisfied by real world data. The low performance of the model was because the data did not obey the variance = mean criterion required of it by the Poisson regression model. Training summary for the Poisson regression model showing unacceptably high values for deviance and Pearson chi-squared statistics (Image by Author)
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