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.
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