Problems not suitable for machine learning -


i know there lot of problem suitable machine learning, problem not suitable it?

when should not use machine learning?

too broad question so, i'll try answer theoretically: problem in ml generalization on unseen data, has pattern, because can info (predict label, cluster) new unseen data if learned distribution before. theoretically can force machine learn task has non-random distribution relative available features, need have enough data desired accuracy. that's why it's impossible in practice. because capture hard distribution rules need have big variative (more universal), or more specific (but it's includes own knowledge data) model, , learn model without overfitting need have big dataset, of need have powerful computational resources , on.

if interested in more detailed explanation theoretical aspect, can watch lectures caltech, starting caltechx - cs1156x learning data.

also there theoretical equations predict generalization abilities of model respect available amount of data: vapnik–chervonenkis theory akaike_information_criterion


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