sklearn sk0.23-0.3.1 (latest)

val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

module ColumnTransformer : sig ... end

module TransformedTargetRegressor : sig ... end

module Make_column_selector : sig ... end

val make_column_transformer : 
  ?kwargs:(string * Py.Object.t) list ->
  ([> `TransformerMixin ] Np.Obj.t
   * [ `S of string
     | `I of int
     | `Ss of string list
     | `Is of int list
     | `Slice of Np.Wrap_utils.Slice.t
     | `Arr of [> `ArrayLike ] Np.Obj.t
     | `Callable of Py.Object.t ])
    list ->
  ColumnTransformer.t

Construct a ColumnTransformer from the given transformers.

This is a shorthand for the ColumnTransformer constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting with ``transformer_weights``.

Read more in the :ref:`User Guide <make_column_transformer>`.

Parameters ---------- *transformers : tuples Tuples of the form (transformer, columns) specifying the transformer objects to be applied to subsets of the data.

transformer : 'drop', 'passthrough' or estimator Estimator must support :term:`fit` and :term:`transform`. Special-cased strings 'drop' and 'passthrough' are accepted as well, to indicate to drop the columns or to pass them through untransformed, respectively. columns : str, array-like of str, int, array-like of int, slice, array-like of bool or callable Indexes the data on its second axis. Integers are interpreted as positional columns, while strings can reference DataFrame columns by name. A scalar string or int should be used where ``transformer`` expects X to be a 1d array-like (vector), otherwise a 2d array will be passed to the transformer. A callable is passed the input data `X` and can return any of the above. To select multiple columns by name or dtype, you can use :obj:`make_column_selector`.

remainder : 'drop', 'passthrough' or estimator, default='drop' By default, only the specified columns in `transformers` are transformed and combined in the output, and the non-specified columns are dropped. (default of ``'drop'``). By specifying ``remainder='passthrough'``, all remaining columns that were not specified in `transformers` will be automatically passed through. This subset of columns is concatenated with the output of the transformers. By setting ``remainder`` to be an estimator, the remaining non-specified columns will use the ``remainder`` estimator. The estimator must support :term:`fit` and :term:`transform`.

sparse_threshold : float, default=0.3 If the transformed output consists of a mix of sparse and dense data, it will be stacked as a sparse matrix if the density is lower than this value. Use ``sparse_threshold=0`` to always return dense. When the transformed output consists of all sparse or all dense data, the stacked result will be sparse or dense, respectively, and this keyword will be ignored.

n_jobs : int, default=None 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.

verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed.

Returns ------- ct : ColumnTransformer

See also -------- sklearn.compose.ColumnTransformer : Class that allows combining the outputs of multiple transformer objects used on column subsets of the data into a single feature space.

Examples -------- >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder >>> from sklearn.compose import make_column_transformer >>> make_column_transformer( ... (StandardScaler(), 'numerical_column'), ... (OneHotEncoder(), 'categorical_column')) ColumnTransformer(transformers=('standardscaler', StandardScaler(...), ['numerical_column']), ('onehotencoder', OneHotEncoder(...), ['categorical_column']))

package sklearn