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# `sklearn.utils`.check_X_y

sklearn.utils.check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, multi_output=False, ensure_min_samples=1, ensure_min_features=1, y_numeric=False, estimator=None)

  Input validation for standard estimators.

  Checks X and y for consistent length, enforces X to be 2D and y 1D. By default, X is checked to be non-empty and containing only finite values. Standard input checks are also applied to y, such as checking that y does not have np.nan or np.inf targets. For multi-label y, set multi_output=True to allow 2D and sparse y. If the dtype of X is object, attempt converting to float, raising on failure.

  Parameters:
    X{ndarray, list, sparse matrix}

      Input data.

    y{ndarray, list, sparse matrix}

      Labels.

    accept_sparse str, bool or list of str, default=False

      String[s] representing allowed sparse matrix formats, such as ‘csc’, ‘csr’, etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.

    accept_large_sparse bool, default=True

      If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse will cause it to be accepted only if its indices are stored with a 32-bit dtype.

      New in version 0.20.

    dtype‘numeric’, type, list of type or None, default=’numeric’

      Data type of result. If None, the dtype of the input is preserved. If “numeric”, dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.

    order{‘F’, ‘C’}, default=None

      Whether an array will be forced to be fortran or c-style. If `None`, then the input data’s order is preserved when possible.

    copy bool, default=False

      Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

    force_all_finite bool or ‘allow-nan’, default=True

      Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter does not influence whether y can have np.inf, np.nan, pd.NA values. The possibilities are:

      - True: Force all values of X to be finite.

      - False: accepts np.inf, np.nan, pd.NA in X.

      - ‘allow-nan’: accepts only np.nan or pd.NA values in X. Values cannot be infinite.

      New in version 0.20: `force_all_finite` accepts the string `'allow-nan'`.

      Changed in version 0.23: Accepts `pd.NA` and converts it into `np.nan`

    ensure_2d bool, default=True

      Whether to raise a value error if X is not 2D.

    allow_nd bool, default=False

      Whether to allow X.ndim > 2.

    multi_output bool, default=False

      Whether to allow 2D y (array or sparse matrix). If false, y will be validated as a vector. y cannot have np.nan or np.inf values if multi_output=True.

    ensure_min_samples int, default=1

      Make sure that X has a minimum number of samples in its first axis (rows for a 2D array).

    ensure_min_features int, default=1

      Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when X has effectively 2 dimensions or is originally 1D and `ensure_2d` is True. Setting to 0 disables this check.

    y_numeric bool, default=False

      Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms.

    estimator str or estimator instance, default=None

      If passed, include the name of the estimator in warning messages.

  Returns:
    X_converted object

      The converted and validated X.

    y_converted object

      The converted and validated y.