# One Hot Encoding

## One Hot Encoding :

One hot encoding is a technique used to represent categorical variables in a machine learning model. It is called one hot encoding because it creates a new dummy variable for each unique category in the categorical variable, and assigns a “1” to the dummy variable corresponding to the category the observation belongs to, and “0” to all other dummy variables.
Here are two examples to illustrate one hot encoding:
Example 1:
Suppose we have a dataset containing information about different animals, including the species of each animal. The species variable is a categorical variable with three categories: “Dog”, “Cat”, and “Bird”. Using one hot encoding, we would create three new dummy variables: “species_Dog”, “species_Cat”, and “species_Bird”. If an animal is a dog, the “species_Dog” dummy variable would be 1 and the other two dummy variables would be 0. If an animal is a cat, the “species_Cat” dummy variable would be 1 and the other two dummy variables would be 0. If an animal is a bird, the “species_Bird” dummy variable would be 1 and the other two dummy variables would be 0.
Example 2:
Suppose we have a dataset containing information about customers at a store, including their gender. The gender variable is a categorical variable with two categories: “Male” and “Female”. Using one hot encoding, we would create two new dummy variables: “gender_Male” and “gender_Female”. If a customer is male, the “gender_Male” dummy variable would be 1 and the “gender_Female” dummy variable would be 0. If a customer is female, the “gender_Female” dummy variable would be 1 and the “gender_Male” dummy variable would be 0.
One hot encoding is often used in machine learning models because most algorithms can’t handle categorical variables directly. By encoding the categorical variables into a series of binary variables, we can use these variables as input to the model. One hot encoding can also help prevent the model from making incorrect assumptions about the relative importance of the categories. For example, if we didn’t use one hot encoding and simply encoded the gender variable as “Male”=1 and “Female”=2, the model might assume that males are twice as important as females, which is not the case.
One hot encoding is not always the best choice for encoding categorical variables. In some cases, it can create a large number of dummy variables, which can make the model more complex and harder to interpret. Additionally, one hot encoding can create a sparse matrix, where most of the entries are 0, which can be inefficient for some algorithms. In these cases, other encoding techniques such as ordinal encoding or binary encoding may be more suitable.
Overall, one hot encoding is a useful technique for representing categorical variables in a machine learning model, and it is widely used in practice.