class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, **kwargs) [source] Regression based on neighbors within a fixed radius. 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. Parameters: radius : float, optional (default = 1.0) Range of parameter space to use by default for radius_neighbors

class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', outlier_label=None, metric_params=None, **kwargs) [source] Classifier implementing a vote among neighbors within a given radius Read more in the User Guide. Parameters: radius : float, optional (default = 1.0) Range of parameter space to use by default for :meth`radius_neighbors` queries. weights : str or callable weight function used in prediction. P

class sklearn.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1, **kwargs) [source] Unsupervised learner for implementing neighbor searches. Read more in the User Guide. Parameters: n_neighbors : int, optional (default = 5) Number of neighbors to use by default for k_neighbors queries. radius : float, optional (default = 1.0) Range of parameter space to use by default for radius_neighbors queries.

class sklearn.neighbors.NearestCentroid(metric='euclidean', shrink_threshold=None) [source] Nearest centroid classifier. Each class is represented by its centroid, with test samples classified to the class with the nearest centroid. Read more in the User Guide. Parameters: metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by metrics.pairwise.pairwise_distan

class sklearn.neighbors.LSHForest(n_estimators=10, radius=1.0, n_candidates=50, n_neighbors=5, min_hash_match=4, radius_cutoff_ratio=0.9, random_state=None) [source] Performs approximate nearest neighbor search using LSH forest. LSH Forest: Locality Sensitive Hashing forest [1] is an alternative method for vanilla approximate nearest neighbor search methods. LSH forest data structure has been implemented using sorted arrays and binary search and 32 bit fixed-length hashes. Random projection

class sklearn.neighbors.KNeighborsRegressor(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs) [source] 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. Parameters: n_neighbors : int, optional (default = 5) Number of neighbors to use by default for k_neighbors queries. we

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs) [source] Classifier implementing the k-nearest neighbors vote. Read more in the User Guide. Parameters: n_neighbors : int, optional (default = 5) Number of neighbors to use by default for k_neighbors queries. weights : str or callable, optional (default = ?uniform?) weight function used in prediction. Possible val

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None) [source] Kernel Density Estimation Read more in the User Guide. Parameters: bandwidth : float The bandwidth of the kernel. algorithm : string The tree algorithm to use. Valid options are [?kd_tree?|?ball_tree?|?auto?]. Default is ?auto?. kernel : string The kernel to use. Valid kernels are [?gaussian?|?topha

class sklearn.neighbors.KDTree KDTree for fast generalized N-point problems KDTree(X, leaf_size=40, metric=?minkowski?, **kwargs) Parameters: X : array-like, shape = [n_samples, n_features] n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made. leaf_size : positive integer (default = 40) Number of points at which to swi

class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=?minkowski?, **kwargs) Parameters: X : array-like, shape = [n_samples, n_features] n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made. leaf_size : positive integer (default = 40) Number of points at which