Class: KNeighborsRegressor
Regression based on k-nearest neighbors.
The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
Read more in the User Guide.
Constructors
new KNeighborsRegressor()
new KNeighborsRegressor(
opts?):KNeighborsRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "auto" | "ball_tree" | "kd_tree" | "brute" | Algorithm used to compute the nearest neighbors: |
opts.leaf_size? | number | Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. |
opts.metric? | string | Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. See the documentation of scipy.spatial.distance and the metrics listed in distance_metrics for valid metric values. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string. If metric is a DistanceMetric object, it will be passed directly to the underlying computation routines. |
opts.metric_params? | any | Additional keyword arguments for the metric function. |
opts.n_jobs? | number | The number of parallel jobs to run for neighbors search. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. Doesn’t affect fit method. |
opts.n_neighbors? | number | Number of neighbors to use by default for kneighbors queries. |
opts.p? | number | Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. |
opts.weights? | "uniform" | "distance" | Weight function used in prediction. Possible values: |
Returns KNeighborsRegressor
Defined in generated/neighbors/KNeighborsRegressor.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/neighbors/KNeighborsRegressor.ts:23 |
_isInitialized | boolean | false | generated/neighbors/KNeighborsRegressor.ts:22 |
_py | PythonBridge | undefined | generated/neighbors/KNeighborsRegressor.ts:21 |
id | string | undefined | generated/neighbors/KNeighborsRegressor.ts:18 |
opts | any | undefined | generated/neighbors/KNeighborsRegressor.ts:19 |
Accessors
effective_metric_
Get Signature
get effective_metric_():
Promise<string>
The distance metric to use. It will be same as the metric parameter or a synonym of it, e.g. ‘euclidean’ if the metric parameter set to ‘minkowski’ and p parameter set to 2.
Returns Promise<string>
Defined in generated/neighbors/KNeighborsRegressor.ts:456
effective_metric_params_
Get Signature
get effective_metric_params_():
Promise<any>
Additional keyword arguments for the metric function. For most metrics will be same with metric_params parameter, but may also contain the p parameter value if the effective_metric_ attribute is set to ‘minkowski’.
Returns Promise<any>
Defined in generated/neighbors/KNeighborsRegressor.ts:483
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/neighbors/KNeighborsRegressor.ts:537
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/neighbors/KNeighborsRegressor.ts:510
n_samples_fit_
Get Signature
get n_samples_fit_():
Promise<number>
Number of samples in the fitted data.
Returns Promise<number>
Defined in generated/neighbors/KNeighborsRegressor.ts:564
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/neighbors/KNeighborsRegressor.ts:88
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/neighbors/KNeighborsRegressor.ts:144
fit()
fit(
opts):Promise<any>
Fit the k-nearest neighbors regressor from the training dataset.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.y? | any | Target values. |
Returns Promise<any>
Defined in generated/neighbors/KNeighborsRegressor.ts:161
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/neighbors/KNeighborsRegressor.ts:202
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/neighbors/KNeighborsRegressor.ts:101
kneighbors()
kneighbors(
opts):Promise<ArrayLike[]>
Find the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.n_neighbors? | number | Number of neighbors required for each sample. The default is the value passed to the constructor. |
opts.return_distance? | boolean | Whether or not to return the distances. |
opts.X? | any | The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. |
Returns Promise<ArrayLike[]>
Defined in generated/neighbors/KNeighborsRegressor.ts:240
kneighbors_graph()
kneighbors_graph(
opts):Promise<any[]>
Compute the (weighted) graph of k-Neighbors for points in X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.mode? | "connectivity" | "distance" | Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are distances between points, type of distance depends on the selected metric parameter in NearestNeighbors class. |
opts.n_neighbors? | number | Number of neighbors for each sample. The default is the value passed to the constructor. |
opts.X? | any | The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. For metric='precomputed' the shape should be (n_queries, n_indexed). Otherwise the shape should be (n_queries, n_features). |
Returns Promise<any[]>
Defined in generated/neighbors/KNeighborsRegressor.ts:288
predict()
predict(
opts):Promise<ArrayLike>
Predict the target for the provided data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Test samples. |
Returns Promise<ArrayLike>
Defined in generated/neighbors/KNeighborsRegressor.ts:336
score()
score(
opts):Promise<number>
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. |
opts.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/neighbors/KNeighborsRegressor.ts:372
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>