KNNImputer
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new KNNImputer(opts?: object): KNNImputer;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.add_indicator? | boolean | 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. Default Value false |
opts.copy? | boolean | If true, a copy of X will be created. If false, imputation will be done in-place whenever possible. Default Value true |
opts.keep_empty_features? | boolean | 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. Default Value false |
opts.metric? | "nan_euclidean" | Distance metric for searching neighbors. Possible values: Default Value 'nan_euclidean' |
opts.missing_values? | string | number | 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. |
opts.n_neighbors? | number | Number of neighboring samples to use for imputation. Default Value 5 |
opts.weights? | "uniform" | "distance" | Weight function used in prediction. Possible values: Default Value 'uniform' |
Returns
Defined in: generated/impute/KNNImputer.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/impute/KNNImputer.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/impute/KNNImputer.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/impute/KNNImputer.ts:21 (opens in a new tab)
id
string
Defined in: generated/impute/KNNImputer.ts:18 (opens in a new tab)
opts
any
Defined in: generated/impute/KNNImputer.ts:19 (opens in a new tab)
Accessors
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/impute/KNNImputer.ts:392 (opens in a new tab)
indicator_
Indicator used to add binary indicators for missing values. undefined if add_indicator is false.
Signature
indicator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/impute/KNNImputer.ts:344 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/impute/KNNImputer.ts:367 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/impute/KNNImputer.ts:77 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/impute/KNNImputer.ts:81 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/impute/KNNImputer.ts:136 (opens in a new tab)
fit()
Fit the imputer on X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Input data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/impute/KNNImputer.ts:153 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/impute/KNNImputer.ts:193 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/impute/KNNImputer.ts:240 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/impute/KNNImputer.ts:90 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/impute/KNNImputer.ts:278 (opens in a new tab)
transform()
Impute all missing values in X.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input data to complete. |
Returns
Promise<ArrayLike[]>
Defined in: generated/impute/KNNImputer.ts:311 (opens in a new tab)