# Does machine learning offer alternatives to linear regression (i.e., OLS) for predicting continuous variables? [closed]

I understand that defining the scope of what falls precisely under the scope 'machine learning' is impossible.

Yet, are there are insights from the machine learning literature that moves away from linear regression using OLS when the goal is predicting a continuous variable?

It seems that machine learning offers a lot more options (relative to models typically described in econometrics literature) when it comes to predicting categorical or binary outcomes, than continuous variables.

• I feel that reading a review of machine learning methods could provide you with many examples. web.stanford.edu/~hastie/ElemStatLearn – Sycorax says Reinstate Monica Feb 18 at 17:37
• No source, thus just a comment: ML (more or less) originated from categorical prediction. Many methods have been generalized to continuous predictions by now, but way back this was not the original intent. Under the hood, many methods still use categorical variables even if predicting continuous ones. – Eulenfuchswiesel Feb 19 at 10:38

It is not true. There's similar number of regression algorithms as classification algorithms in machine learning. Most of the classification algorithms have their regression counterparts: there's $$k$$-NN and $$k$$-NN regression, SVM's and SVR's for regression, random forest build of classification, or regression trees, XGBoost can be used for both tasks, there's an infinite number of regression neural networks, etc.