When we use one hot encoding, there is an increase in the dimensionality of a dataset. The reason for the increase in dimensionality is that, for every class in the categorical variables, it forms a different variable.
Example: Suppose, there is a variable ‘Color.’ It has three sub-levels as Yellow, Purple, and Orange. So, one hot encoding ‘Color’ will create three different variables as Color.Yellow, Color.Porple, and Color.Orange.
In label encoding, the sub-classes of a certain variable get the value as 0 and 1. So, we use label encoding only for binary variables.
This is the reason that one hot encoding increases the dimensionality of data and label encoding does not.
Now, if you are interested in doing an end-to-end certification course in Machine Learning, you can check out Intellipaat’s Machine Learning Course with Python.
