Decision Trees
Decision trees are mostly used for classification tasks. They are a non-parametric form of supervised learning method, meaning that unlike in SVM where you had to specify the kernel type, C, gamma, and other parameters, there are no such parameters to be specified in the case of decision trees. This also makes them quite easy to work with. Decision trees, as the name suggests, use a tree-based structure for making a decision (finding the target variable). Each "branch" of the decision tree is made by following a rule, for example, "is some feature more than some value? – yes or no." Decision trees can be used both as regressors and classifiers with minimal changes. The following are the advantages and disadvantages of using decision trees for classification:
Advantages
- Decision trees are easy to understand and visualize.
- They can handle both numeric and categorical data.
- The requirement for data cleaning in the case of decision...
