One-Hot Encoder¶
Encode categorical integer features using a one-hot aka one-of-K scheme.
For each categorical input feature, a number of output features will be given of which exactly one is marked as true and the rest as false. 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. Also note: categories for the input data are generated automatically (as in category=’auto’ keyword in scikit-learn)
Documentation¶
Attributes¶
active_features_
- categories_
The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of
transform
). This includes the category specified indrop
(if any).feature_indices_
n_values_
Definition¶
Output ports¶
- model model
Model
Configuration¶
- Handle unknown (handle_unknown)
How to handle unknown categories during (non-fit) transform
- Transformed array in sparse format (sparse)
Will generate sparse matrix if true. Warning: sparse matrices are not handled by all Sympathy nodes and may be silently converted to non-sparse arrays
Implementation¶
- class node_preprocessing.OneHotEncoder[source]