Quote:
Originally Posted by acorn54
i understand that multinominal logistic regression is not the silver bullet for paths to profits. the 64k question is when to know what model to use for best evaluation.
|
There is no analytical methodology to decide "A priori" whether logistic regression will be sufficient for your problem or not. Usually the only way is to try several different approaches and pick the best among them (or create an "
ensemble" of the most promising solutions and use all of them based on some type of a voting mechanism. There exist problems that fit very well in linear (or logistic) regression and there are others that are impossible to be solved without the introduction of "hidden" processing layers as it happens in Deep Learning NN for example. An example of the former, is the creation of a par-times model that predicts the final time of a race based on factors like distance, classification, age and sex while a typical example of the latter can be found in the solution of the XOR operator, something that is impossible to achieve using logistic regression without the addition of a hidden layer (perceptron).
