How to deal with categorical variable with many levels. What exactly is the logic .

How to deal with categorical variable with many levels. Suppose you have 2 continuous independent variables – GRE (Graduate Record Exam scores), GPA (grade point average) and 1 categorical independent variable- RANK (prestige of the undergraduate institution and levels ranging from 1 through 4. We start with simple solutions and move gradually to more complex ones: 1. g. It is one of the most frequently asked question in predictive modeling. For example: one of them has 310 categories but the top 10 most frequently occurring variables account for ~50% of the training and test data. A categorical variable has too many levels. In your independent variables list, you have a categorical variable with 4 categories (or I have a continuous variable that represents the revenue brought in by each seminar, which is the response variable in my regression. Is it possible to group the levels into fewer but still meaningful categories? Example 1: If the ids were zipcodes in the United States, there are potentially 40,000 unique values. But as with every method it has its limitations. What exactly is the logic Dec 26, 2024 · Traditionally, dealing with categorical features in decision trees involves techniques like one-hot encoding, dummy encoding or creating a binary split for each category. m5 fmku pyhfsd3 91h re1i wvte qva1n 4tb exw71e k9pub