I got this error because of an issue with the parallel computing back-end for foreach/dopar. You may have some parallel computing going on in the background that is not getting cleaned up fully between runs. The easiest way I have found to fix it is to call this function: This function is from Steve Weston’s … Read more
You can’t have factor/categorical response variables. Illustration: If you really want to do a logistic regression you should change them to 0 and 1, or FALSE and TRUE, and use family=binomial:
scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.
They’re both correct, but yours is preferred from the point of view of numerical stability. You start with By using the fact that a^(b – c) = (a^b)/(a^c) we have Which is what the other answer says. You could replace max(x) with any variable and it would cancel out.
You added the as.factor(dataCancer$Classification) in the script, but even if the dataset dataCancer is attached, a command like the one above does not transform the dataset variable Classification into a factor. It only returns a factor on the console. Since you want to fit the model on the training dataset, you either specify or use the as.factor function in the glm line code