Decision trees are an effective and user-friendly tool that is used in the field of machine learning as well as data mining to aid in both regression and classification tasks. They excel with categorical variables, which are variables that are assigned a limitless set of values, typically representing groups or categories. Data Science Course in Pune
This article will dig into the way decision trees deal with categorical variables, examining the mechanisms behind them, popular algorithms, and the best methods.
Understanding Decision TreesHierarchical decision trees are made up of edges and nodes. The nodes represent a particular decision that is based on a particular feature and each edge represents the possibilities of a decision. At the base of the tree, there is a first decision. As you move through the tree, decisions are taken based on the features until a final choice or prediction is made in the leaf nodes.
Handling Categorical VariablesCategorical variables present a particular problem in the construction of decision trees since they are not able to be directly evaluated in the same manner as numerical ones. But, decision trees manage their challenges effectively by recursively dividing the data according to the categorical variables.
Binary SplittingOne method for dealing with categorical variables is by using binary splitting. This is the process of dividing your data into two categories depending on whether the categorical variable belongs to a specific class or not. The process repeats until a stop criterion has been reached, for example, reaching the maximum tree depth or the minimum number of samples per node.
Multiway SplittingIn certain instances, the decision tree can be used to split categorical variables that have different categories. As opposed to splits in binary each category is a distinct branch within the tree. This method lets decision trees deal with categorical variables that span at least two different categories efficiently.