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 change 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).