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Regression based on knearest neighbors. This node has been automatically generated by wrapping the ``sklearn.neighbors.regression.KNeighborsRegressor`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set. Read more in the :ref:`User Guide <regression>`. **Parameters** n_neighbors : int, optional (default = 5) Number of neighbors to use by default for :meth:`k_neighbors` queries. weights : str or callable 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 userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights. Uniform weights are used by default. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors:  'ball_tree' will use :class:`BallTree`  'kd_tree' will use :class:`KDtree`  'brute' will use a bruteforce search.  'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, optional (default = 30) 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. metric : string or DistanceMetric object (default='minkowski') the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics. p : integer, optional (default = 2) 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. metric_params : dict, optional (default = None) Additional keyword arguments for the metric function. n_jobs : int, optional (default = 1) The number of parallel jobs to run for neighbors search. If ``1``, then the number of jobs is set to the number of CPU cores. Doesn't affect :meth:`fit` method. **Examples** >>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsRegressor >>> neigh = KNeighborsRegressor(n_neighbors=2) >>> neigh.fit(X, y) # doctest: +ELLIPSIS KNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [ 0.5] See also NearestNeighbors RadiusNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier **Notes** See :ref:`Nearest Neighbors <neighbors>` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``. .. warning:: Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor `k+1` and `k`, have identical distances but but different labels, the results will depend on the ordering of the training data. http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm














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_train_seq List of tuples: 

dtype dtype 

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output_dim Output dimensions 

supported_dtypes Supported dtypes 

Regression based on knearest neighbors. This node has been automatically generated by wrapping the ``sklearn.neighbors.regression.KNeighborsRegressor`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set. Read more in the :ref:`User Guide <regression>`. **Parameters** n_neighbors : int, optional (default = 5) Number of neighbors to use by default for :meth:`k_neighbors` queries. weights : str or callable 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 userdefined function which accepts an array of distances, and returns an array of the same shape containing the weights. Uniform weights are used by default. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors:  'ball_tree' will use :class:`BallTree`  'kd_tree' will use :class:`KDtree`  'brute' will use a bruteforce search.  'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, optional (default = 30) 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. metric : string or DistanceMetric object (default='minkowski') the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics. p : integer, optional (default = 2) 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. metric_params : dict, optional (default = None) Additional keyword arguments for the metric function. n_jobs : int, optional (default = 1) The number of parallel jobs to run for neighbors search. If ``1``, then the number of jobs is set to the number of CPU cores. Doesn't affect :meth:`fit` method. **Examples** >>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsRegressor >>> neigh = KNeighborsRegressor(n_neighbors=2) >>> neigh.fit(X, y) # doctest: +ELLIPSIS KNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [ 0.5] See also NearestNeighbors RadiusNeighborsRegressor KNeighborsClassifier RadiusNeighborsClassifier **Notes** See :ref:`Nearest Neighbors <neighbors>` in the online documentation for a discussion of the choice of ``algorithm`` and ``leaf_size``. .. warning:: Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor `k+1` and `k`, have identical distances but but different labels, the results will depend on the ordering of the training data. http://en.wikipedia.org/wiki/Knearest_neighbor_algorithm




Predict the target for the provided data This node has been automatically generated by wrapping the sklearn.neighbors.regression.KNeighborsRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit the model using X as training data and y as target values This node has been automatically generated by wrapping the sklearn.neighbors.regression.KNeighborsRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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