What is the difference between LabelBinarizer vs. OneHotEncoder
Answer: LabelBinarizer encodes single-label categories as one-hot vectors, while OneHotEncoder handles multi-label categories across multiple columns.
Let’s break down the differences in more detail:
|
Features |
LabelBinarizer |
OneHotEncoder |
|---|---|---|
|
Input |
Single-column categorical variable |
Multi-column categorical variables |
|
Handling of multiple labels |
It does not handle multiple columns |
Handles multiple columns simultaneously |
|
Encoding method |
Converts each label into a binary vector |
Creates a binary matrix for each category |
|
Suitable for |
Binary classification, ordinal variables |
Non-ordinal categorical variables |
|
Example: Original Data |
[‘red’, ‘blue’, ‘green’] |
[[‘red’, ‘large’], [‘blue’, ‘small’]] |
|
Example: Encoded Data |
[[1, 0, 0], [0, 1, 0], [0, 0, 1]] |
[[1, 0, 0, 0, 1], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0]] |
In the example above, for the LabelBinarizer, each color in the original data is transformed into a binary vector. Meanwhile, the OneHotEncoder creates a binary matrix where each category occupies a column, and the presence or absence of each category is represented by 1 or 0, respectively, across multiple columns.
Conclusion
In summary, the LabelBinarizer is simpler and more suitable for binary classification or ordinal categorical variables, while the OneHotEncoder is more versatile and appropriate for handling non-ordinal categorical variables with multiple categories. The choice between them depends on the specific nature of the data and the requirements of the machine learning task.
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