What is difference between one hot encoding and leave one out encoding?

Answer: One-hot encoding represents each category with a binary vector, while leave-one-out encoding replaces a category with the mean of the target variable excluding the current observation.

One-hot encoding and leave-one-out encoding are two different methods used in categorical variable encoding. Let’s compare them in detail in tabular form:

Criteria	One-Hot Encoding	Leave-One-Out Encoding
Concept	Represents each category as a binary column, where only one column is ‘1’ (hot) and the rest are ‘0’.	Encodes a categorical variable by leaving one category out in each encoding, resulting in a numerical representation.
Number of Columns	Number of columns equals the number of unique categories in the variable.	Number of columns equals the number of unique categories minus one.
Sparsity	Generates a sparse matrix with mostly ‘0’ values, as only one column is ‘1’ for each observation.	Generally less sparse compared to one-hot encoding, as one column is omitted for each observation.
Collinearity	May lead to multicollinearity issues since the presence of one variable can be perfectly predicted from the others.	Reduces collinearity issues, as one category is omitted, providing linearly independent features.
Interpretability	Each category has a distinct column, making interpretation straightforward.	Interpretability may be more challenging as the encoded values are derived based on leaving out one category.
Computational Complexity	Can be computationally expensive when dealing with a large number of unique categories.	Generally less computationally expensive as it involves fewer columns and may be more efficient for large datasets.
Use Cases	Suitable for scenarios where interpretability and the individual impact of each category are essential.	Useful when dealing with multicollinearity issues and when a simpler, less sparse representation is desired.
Example	Consider a variable “Color” with categories: Red, Green, Blue. Encoded as: Red: [1, 0, 0], Green: [0, 1, 0], Blue: [0, 0, 1].	If leaving out ‘Green’, the encoding for “Color” would be: Red: [1, 0], Blue: [0, 1].

Conclusion:

• One-Hot Encoding: Suitable for scenarios where interpretability is crucial, but it can lead to multicollinearity issues due to the presence of redundant columns.
• Leave-One-Out Encoding: Addresses multicollinearity concerns by excluding one category in the encoding. It is generally less sparse and computationally efficient compared to one-hot encoding, making it suitable for certain situations.