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TEST7c4f62a543sklearn.preprocessing._encoders.OneHotEncoder

TEST7c4f62a543sklearn.preprocessing._encoders.OneHotEncoder

Visibility: public Uploaded 15-01-2024 by Continuous Integration sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 0 runs
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Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the ``sparse`` parameter) By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the `categories` manually. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels. Note: a one-hot encoding of y labels should use a LabelBinarizer instead.

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