17485 10987 sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder) sklearn.ColumnTransformer sklearn.compose._column_transformer.ColumnTransformer 1 openml==0.10.2,sklearn==0.22.1 Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer. 2020-01-10T19:14:52 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 n_jobs int or None null Number of jobs to run in parallel ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details remainder "drop" sparse_threshold float 0.3 If the output of the different transformers contains sparse matrices, these will be stacked as a sparse matrix if the overall density is lower than this value. Use ``sparse_threshold=0`` to always return dense. When the transformed output consists of all dense data, the stacked result will be dense, and this keyword will be ignored transformer_weights dict null Multiplicative weights for features per transformer. The output of the transformer is multiplied by these weights. Keys are transformer names, values the weights transformers list of tuples [{"oml-python:serialized_object": "component_reference", "value": {"key": "one_hot_encoder", "step_name": "one_hot_encoder", "argument_1": [true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true]}}] List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data verbose boolean false If True, the time elapsed while fitting each transformer will be printed as it is completed. one_hot_encoder 17483 10987 sklearn.preprocessing._encoders.OneHotEncoder sklearn.OneHotEncoder sklearn.preprocessing._encoders.OneHotEncoder 19 openml==0.10.2,sklearn==0.22.1 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. 2020-01-10T19:00:41 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 categories 'auto' or a list of array "auto" Categories (unique values) per feature: - 'auto' : Determine categories automatically from the training data - list : ``categories[i]`` holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values The used categories can be found in the ``categories_`` attribute drop 'first' or a array null Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into a neural network or an unregularized regression dtype number type {"oml-python:serialized_object": "type", "value": "np.float64"} Desired dtype of output handle_unknown : {'error', 'ignore'}, default='error' Whether to raise an error or ignore if an unknown categorical feature is present during transform (default is to raise). When this parameter is set to 'ignore' and an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. In the inverse transform, an unknown category will be denoted as None. handle_unknown "error" sparse bool true Will return sparse matrix if set True else will return an array openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1