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 Trees
Hierarchical 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 Variables
Categorical 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 Splitting
One 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 Splitting
In 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.
Encoding Categorical Variables
When creating the decision tree typically requires to be encoded in a numerical encoding format. This is usually done with techniques like one-hot encryption or label encoder.
One-Hot Encoding
One-hot encoding is the process of creating an individual binary column for each category in categorical variables. If there is more than n a category, that will result in the creation of n different binary columns in which each column will indicate the presence of a specific category. in existence or not.
Label Encoding
Label encoding assigns a unique number to each category of a categorical variable. This technique converts categorical variables to ordinal ones which might not be appropriate for all algorithms but may be useful using a decision tree.
Splitting Criteria
Decision trees employ a variety of factors to determine the most effective division at any node. For categorical variables, the common splitting criteria are:
- Gini impurity
- Information gain (or entropy)
- Chi-square test
These criteria evaluate the homogeneity and consistency of the target variable in each split and try to improve the accuracy of the resultant nodes.
Handling Missing Values
Another thing to take into consideration in dealing with the categorical variable in decision trees is to deal with missing values. Decision trees can handle missing values by classifying them into the more commonly used category creating a distinct category to accommodate missing values, or using surrogate splits.
Surrogate Splits
Surrogate splits are substitute splits employed when the primary split can’t be used because of the absence of values. Surrogate splits replicate the behavior similar to the original split when they can and keep the predictive capability that the decision tree has.
One 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.
Conclusion
In the end, the decision tree is a versatile model that can handle categorical variables with ease. Through the use of techniques like multiway or binary splitting, decoding categorical variables, determining the appropriate splitting criteria, as well as handling missing values, decision tree models can accurately recognize the relationships between categorical variables as well as the underlying variable. Knowing these processes is crucial to build a solid model of decision trees that can effectively interpret and analyze categorical data across various domains.
