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To start, I'd like to say I have very little experience in machine learning, or statistics/computer science in general.

I am interested in a list of models to classify a binary dependent (response/output/Y) variable with non-ordered categorical independent (explanatory/input/X) variables. I know the list at https://topepo.github.io/caret/train-models-by-tag.html#Neural_Network that has been super helpful, but I can't tell which models use quantitative or ordered variables, or a quantitative output variable.

I've used a random forest and neural network model to some great success, but I'd like to find some other models that I can play/learn with.

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    $\begingroup$ The idea of using a classification algorithm is usually misplaced. It is usually more appropriate to estimate probabilities of outcomes. $\endgroup$ Mar 26 at 12:55

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In theory you can use also all algorithms having categorical data. You would need to encode them in a way the algorithm can read the information. This is often done using one-hot-encoding, but this can be very computational expensive if your data set is big.

Have you tried xgboost and catboost? Especially catboost was developed handling categorical features and there is no pre-processing needed from your side. I would for sure try catboost (and if you have more time I would also try xgboost).

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