I want to understand the criteria of selection of ML algorithms, i.e., what are the guidelines on which algorithm to be selected in which case?
The reasons I know are:
- Logistic regression to be picked in case we want to advise the impact on y variable on what changes on any x variable.
- Random forest works good on mixed data and very effective for categorical data. Also, it does feature selection first (so dimension reduction is not needed).
- Random forest not to be picked with high featured and multiple category data due to its high processing time.
- SVM works well with the closely placed data points like in image processing identification of dog vs cat.
But these are not sufficient enough to pick anyone, as I don't have any reason for why which algorithm not to be picked, like when to choose SVM over Logistic regression or RF over Logistic regression.
The only rationale I have is the performance, so I run all algorithms and who ever performs best that I select (but this is not right way).