One Hot & Dummy Encoding in Python ML 🐍

One Hot Encoding and Dummy Encoding Machine Learning Python Pandas SkLearn by Dr. Mahesh Huddar

One Hot Encoding:
In one-hot encoding, we create a new set of dummy (binary) variables that is equal to the number of categories (k) in the variable.
For example, let’s say we have a categorical variable Color with three categories called “Red”, “Green” and “Blue”, we need to use three dummy variables to encode this variable using one-hot encoding.
A dummy (binary) variable just takes the value 0 or 1 to indicate the exclusion or inclusion of a category.

Dummy encoding
Dummy encoding also uses dummy (binary) variables.
Instead of creating a number of dummy variables that is equal to the number of categories (k) in the variable, dummy encoding uses k-1 dummy variables.
To encode the same Color variable with three categories using the dummy encoding, we need to use only two dummy variables.

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