What is distinct categorical data?
Categorical variables contain a finite number of categories or distinct groups. Categorical data might not have a logical order. Discrete variables are numeric variables that have a countable number of values between any two values. A discrete variable is always numeric.
How many values can a categorical variable have?
Categorical variables with more than two possible values are called polytomous variables; categorical variables are often assumed to be polytomous unless otherwise specified.
How are categorical variables with many possible values solved?
The described problem faces the same problem as you do: As input you have only categorical variables (row ID and column ID are categorical) and each categorical variable has many possible values (number of rows and number of columns). How does this problem is solved? One standard way to solve this problem is a matrix factorization .
How to deal with categorical variable in predictive modeling?
Here are commonly used ones: Using Business Logic: It is one of the most effective method of combining levels. It makes sense also to combine similar levels into similar groups based on domain or business experience. For example, we can combine levels of a variable “zip code” at state or district level.
Are there any discrete values in categorical data?
These discrete values can be text or numeric in nature (or even unstructured data like images!). There are two major classes of categorical data, nominal and ordinal. In any nominal categorical data attribute, there is no concept of ordering amongst the values of that attribute.
Why are m distinct labels important for categorical data?
Essential for any categorical feature of m distinct labels, you get m separate features. This can easily increase the size of the feature set causing problems like storage issues, model training problems with regard to time, space and memory.