Decision Trees
Decision trees are mostly used for classification tasks. They are a non-parametric form of supervised learning method. Decision trees work on the concept of finding out the target variable by learning simple decision rules from data. They can be used for both classification and regression tasks. The following are the advantages and disadvantages of using decision tress for classification:
Advantages
Decision trees are very simple to understand and can be visualized.
They can handle both numeric and categorical data.
The requirement for data cleaning in the case of decision trees is very low since it is able to handle missing data.
It's a non-parametric machine learning algorithm that makes no assumption of space distribution and classifier structures.
It's a white box model rather than a black box model like neural networks, and is able to explain the logic of split using Boolean values.
Disadvantages
Decision trees tend to overfit data very easily, and pruning is required to...
