Skip to content

KNNRegressor

K-Nearest Neighbors regressor.

Samples are stored using a first-in, first-out strategy. The strategy to perform search queries in the data buffer is defined by the engine parameter. Predictions are obtained by aggregating the values of the closest n_neighbors stored samples with respect to a query sample.

Parameters

  • n_neighbors

    Type → int

    Default → 5

    The number of nearest neighbors to search for.

  • engine

    Type → BaseNN | None

    Default → None

    The search engine used to store the instances and perform search queries. Depending on the choose engine, search will be exact or approximate. Please, consult the documentation of each available search engine for more details on its usage. By default, use the SWINN search engine for approximate search queries.

  • aggregation_method

    Type → str

    Default → mean

    The method to aggregate the target values of neighbors. | 'mean' | 'median' | 'weighted_mean'

Examples

from river import datasets
from river import evaluate
from river import metrics
from river import neighbors
from river import preprocessing

dataset = datasets.TrumpApproval()

model = neighbors.KNNRegressor()
evaluate.progressive_val_score(dataset, model, metrics.RMSE())

RMSE: 1.427743

Methods

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

  • x — 'dict'
  • y — 'base.typing.RegTarget'

Returns

Regressor: self

predict_one

Predict the output of features x.

Parameters

  • x — 'dict'
  • kwargs

Returns

base.typing.RegTarget: The prediction.