What is the difference between categorical and continuous?
Categorical variables contain a finite number of categories or distinct groups. Categorical data might not have a logical order. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time.
What are the advantages of categorical data?
The advantage of recoding categorical data as ordinal is that the inferences made from the data are better represent the data and have a more intuitive interpretation. The most useful difference is in displaying results.
What’s the difference between categorical and continuous data?
Data: Continuous vs. Categorical. Data comes in a number of different types, which determine what kinds of mapping can be used for them. The most basic distinction is that between continuous (or quantitative) and categorical data, which has a profound impact on the types of visualizations that can be used.
Which is better for predicting continuous or categorical variables?
Some predictive modeling techniques are more designed for handling continuous predictors, while others are better for handling categorical or discrete variables. Of course there exist techniques to transform one type to another (discretization, dummy variables, etc.).
Is there a distance function for categorical features?
Some known distance function for categorical features: Boriah, S., Chandola and V., Kumar, V. (2008). Similarity measures for categorical data: A comparative evaluation. In: Proceedings of the 8th SIAM International Conference on Data Mining, SIAM, p. 243-254.
Why do decision trees need categorical variables to be?
(It’s equally likely that the tree uses <= and > but that’s just semantics). This obviously works fine for numeric variables, but it does not work well with categorical variables – especially when the categorical variable cannot be ordered in a meaningful way.