Using one hot encoding, the dimensionality (i.e. features) in a dataset get increased because it creates a new variable for each level present in categorical variables. For example: let’s say we have a variable ‘color’. The variable has 3 levels namely Red, Blue, and Green. One hot encoding ‘color’ variable will generate three new variables as Color.Red, Color.Blue and Color.Green containing 0 and 1 value. In label encoding, the levels of categorical variables get encoded as 0 and 1, so no new variable is created. Label encoding is majorly used for binary variables.