18913
6691
sklearn.preprocessing.data.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing.data.OneHotEncoder
30
openml==0.12.2,sklearn==0.18.1
Encode categorical integer features using a one-hot aka one-of-K scheme.
The input to this transformer should be a matrix of integers, denoting
the values taken on by categorical (discrete) features. The output will be
a sparse matrix where each column corresponds to one possible value of one
feature. It is assumed that input features take on values in the range
[0, n_values).
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.
2021-08-13T19:24:49
English
sklearn==0.18.1
numpy>=1.6.1
scipy>=0.9
categorical_features
[3, 4, 7, 8]
dtype
number type
{"oml-python:serialized_object": "type", "value": "np.float64"}
Desired dtype of output
handle_unknown
str
"ignore"
Whether to raise an error or ignore if a unknown categorical feature is
present during transform.
n_values
'auto'
"auto"
Number of values per feature
- 'auto' : determine value range from training data
sparse
boolean
false
Will return sparse matrix if set True else will return an array
openml-python
python
scikit-learn
sklearn
sklearn_0.18.1