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# `sklearn.impute`.KNNImputer

class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False, keep_empty_features=False)

  Imputation for completing missing values using k-Nearest Neighbors.

  Each sample’s missing values are imputed using the mean value from `n_neighbors` nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close.

  Read more in the User Guide.

  New in version 0.22.

  Parameters:
    missing_values int, float, str, np.nan or None, default=np.nan

      The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, `missing_values` should be set to np.nan, since `pd.NA` will be converted to np.nan.

    n_neighbors int, default=5

      Number of neighboring samples to use for imputation.

    weights{‘uniform’, ‘distance’} or callable, default=’uniform’

      Weight function used in prediction. Possible values:

      - ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.

      - ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.

      - callable : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

    metric{‘nan_euclidean’} or callable, default=’nan_euclidean’

      Distance metric for searching neighbors. Possible values:

      - ‘nan_euclidean’

      - callable : a user-defined function which conforms to the definition of `_pairwise_callable(X, Y, metric, **kwds)`. The function accepts two arrays, X and Y, and a `missing_values` keyword in `kwds` and returns a scalar distance value.

    copy bool, default=True

      If True, a copy of X will be created. If False, imputation will be done in-place whenever possible.

    add_indicator bool, default=False

      If True, a `MissingIndicator` transform will stack onto the output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.

    keep_empty_features bool, default=False

      If True, features that consist exclusively of missing values when `fit` is called are returned in results when `transform` is called. The imputed value is always `0`.

      New in version 1.2.

  Attributes:
    indicator_`MissingIndicator`

      Indicator used to add binary indicators for missing values. `None` if add_indicator is False.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `SimpleImputer`

    Univariate imputer for completing missing values with simple strategies.

  `IterativeImputer`

    Multivariate imputer that estimates values to impute for each feature with missing values from all the others.

  References

  - Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525.

  Examples

      >>> import numpy as np
      >>> from sklearn.impute import KNNImputer
      >>> X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]
      >>> imputer = KNNImputer(n_neighbors=2)
      >>> imputer.fit_transform(X)
      array([[1. , 2. , 4. ],
             [3. , 4. , 3. ],
             [5.5, 6. , 5. ],
             [8. , 8. , 7. ]])

  For a more detailed example see Imputing missing values before building an estimator.

  Methods

  `fit`(X[, y]) | Fit the imputer on X.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Impute all missing values in X.

  fit(X, y=None)

    Fit the imputer on X.

    Parameters:
      X array-like shape of (n_samples, n_features)

        Input data, where `n_samples` is the number of samples and `n_features` is the number of features.

      y Ignored

        Not used, present here for API consistency by convention.

    Returns:
      self object

        The fitted `KNNImputer` class instance.

  fit_transform(X, y=None, **fit_params)

    Fit to data, then transform it.

    Fits transformer to `X` and `y` with optional parameters `fit_params` and returns a transformed version of `X`.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.

        - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Impute all missing values in X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input data to complete.

    Returns:
      X array-like of shape (n_samples, n_output_features)

        The imputed dataset. `n_output_features` is the number of features that is not always missing during `fit`.

## Examples using `sklearn.impute.KNNImputer`

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Imputing missing values before building an estimator

Imputing missing values before building an estimator