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

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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