Tag Archives: fuzzy

Classification with Categorical Variables (the fuzzy side)

The Gaussian and the (log) Poisson regressions share a very interesting property,

i.e. the average predicted value is the empirical mean of our sample.

> mean(predict(lm(dist~speed,data=cars)))
[1] 42.98
> mean(cars$dist)
[1] 42.98

One can prove that it is also the prediction for the average individual in our sample

> predict(lm(dist~speed,data=cars),
+ newdata=data.frame(speed=mean(cars$speed))) 

The geometric interpretation is that the regression line passes through the centroid,

> plot(cars)
> abline(lm(dist~speed,data=cars),col="red")
> abline(h=mean(cars$dist),col="blue")
> abline(v=mean(cars$speed),col="blue")
> points(mean(cars$speed),mean(cars$dist))

But in all other cases, it is no longer the case. Consider for instance the case of a logistic regression. And to ask for something even more complicated, consider the case where we have only categorical explanatory variables. In that context, it is more difficult to get a prediction for the “average individual”. Unless we consider some fuzzy interpretation of the regression.

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