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.