# `Scholar.Neighbors.KNNRegressor`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/knn_regressor.ex#L1)

K-Nearest Neighbors Regressor.

Performs regression by computing the (weighted) mean of k-nearest neighbor labels.

# `fit`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/knn_regressor.ex#L81)

Fits a k-NN regressor model.

## Options

* `:algorithm` (`t:atom/0`) - Algorithm used to compute the k-nearest neighbors. Possible values:

    * `:brute` - Brute-force search. See `Scholar.Neighbors.BruteKNN` for more details.

    * `:kd_tree` - k-d tree. See `Scholar.Neighbors.KDTree` for more details.

    * `:random_projection_forest` - Random projection forest. See `Scholar.Neighbors.RandomProjectionForest` for more details.

    * Module implementing `fit(data, opts)` and `predict(model, query)`. predict/2 must return a tuple containing indices
    of k-nearest neighbors of query points as well as distances between query points and their k-nearest neighbors.

  The default value is `:brute`.

* `:weights` - 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.

  The default value is `:uniform`.

Algorithm-specific options (e.g. `:num_neighbors`, `:metric`) should be provided together with the regressor options.

## Examples

    iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> y = Nx.tensor([[1], [2], [3], [4], [5]])
    iex> model = Scholar.Neighbors.KNNRegressor.fit(x, y, num_neighbors: 3)
    iex> model.algorithm
    Scholar.Neighbors.BruteKNN.fit(x, num_neighbors: 3)
    iex> model.labels
    Nx.tensor([[1], [2], [3], [4], [5]])

    iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> y = Nx.tensor([[1], [2], [3], [4], [5]])
    iex> model = Scholar.Neighbors.KNNRegressor.fit(x, y, algorithm: :kd_tree, num_neighbors: 3, metric: {:minkowski, 1})
    iex> model.algorithm
    Scholar.Neighbors.KDTree.fit(x, num_neighbors: 3, metric: {:minkowski, 1})
    iex> model.labels
    Nx.tensor([[1], [2], [3], [4], [5]])

    iex> x = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> y = Nx.tensor([[1], [2], [3], [4], [5]])
    iex> key = Nx.Random.key(12)
    iex> model = Scholar.Neighbors.KNNRegressor.fit(x, y, algorithm: :random_projection_forest, num_neighbors: 2, num_trees: 4, key: key)
    iex> model.algorithm
    Scholar.Neighbors.RandomProjectionForest.fit(x, num_neighbors: 2, num_trees: 4, key: key)
    iex> model.labels
    Nx.tensor([[1], [2], [3], [4], [5]])

# `predict`
[🔗](https://github.com/elixir-nx/scholar/blob/main/lib/scholar/neighbors/knn_regressor.ex#L145)

Predicts labels using a k-NN regressor model.

## Examples

    iex> x_train = Nx.tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
    iex> y_train = Nx.tensor([[1], [2], [3], [4], [5]])
    iex> model = Scholar.Neighbors.KNNRegressor.fit(x_train, y_train, num_neighbors: 3)
    iex> x = Nx.tensor([[1, 3], [4, 2], [3, 6]])
    iex> Scholar.Neighbors.KNNRegressor.predict(model, x)
    Nx.tensor([[2.0], [2.0], [4.0]])

---

*Consult [api-reference.md](api-reference.md) for complete listing*