I'm new to data science and currently trying to learn and understand decision tree algorithm. I have a doubt, how the algoritham works when we have some continuous variables in a classification problem and categorical variables in regression problems. Usually algo works on the basis of gini index in classificaton problems and variance reduction technique in regression problem.
But when it comes to dealing with continuous variable in a classification problem, how the algo consider continuous variable, in the selection of best split (with highest gini index) done. -- vice versa for regression problem
Thanks in advance :)