Applied Machine Learning

$[20$ pts$]$ At a high level $($i$.$e$.,$ without entering into mathematical details$),$ please describe,

compare, and contrast the following classifiers:

Perceptron $($textbook$\text{'}$s version$)$

SVM

Decision Tree

Random Forest $($you have to research a bit about this classifier$)$

Some comparison criterion can be:

Speed?

Strength?

Robustness?

The feature type that the classifier naturally uses $($e$.$g$.$ relying on distance means that

numerical features are naturally used$)$

Is it statistical?

Does the method solve an optimization problem? If yes, what is the cost function?

Which one will be the first that you would try on your dataset?