TD_CategoricalSummary displays the distinct values and their counts for each specified input table column.
A categorical summary refers to a summary of data that is organized into distinct categories or groups. This type of summary is commonly used when dealing with data that is nominal or categorical in nature, such as demographic information, survey responses, or the type of products purchased by customers.
TD_CategoricalSummary can provide information about the number of observations in each category. This information can be presented in tables, charts, histograms, or pie charts, depending on the type of data and the research question.
Categorical summaries can be useful in several stages of the machine learning process, such as data exploration, feature engineering, and model evaluation. They can help identify patterns, relationships, and outliers in the data, as well as guide the selection and transformation of features for the predictive model. They can also be used to assess the performance of the model and compare it with other models or benchmarks.