Which two encodes can be used to transform categories data into numerical features? (Select two.)

Encoding is a technique that transforms categorical data into numerical features that can be used by

machine learning models. Categorical data are data that have a finite number of possible values or

categories, such as gender, color, or country. Encoding can help convert categorical data into a

format that is suitable and understandable for machine learning models. Some of the encoding

methods that can be used to transform categorical data into numerical features are:

Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the

target variable for that category. Mean encoder can capture the relationship between the category

and the target variable, but it may cause overfitting or multicollinearity problems.

One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where

only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can

create distinct and orthogonal vectors for each category, but it may increase the dimensionality and

sparsity of the data.