Visibility: public Uploaded 03-08-2020 by Heinrich Peters sklearn==0.23.1 numpy>=1.6.1 scipy>=0.9 0 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_0.23.1
Issue #Downvotes for this reason By

Loading wiki
Help us complete this description Edit
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.



List all runs
Rendering chart
Rendering table