Difference between revisions of "sklearn.neighbors Module"

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* <B>Context:</B>
 
* <B>Context:</B>
 
** It require to call/select a [[Decision Tree Learning System]] :
 
** It require to call/select a [[Decision Tree Learning System]] :
*** <code>[[sklearn.neighbors]].<span style="font-weight:italic; color:Green">Model_Name(self, arguments)</i></code>  or simply <code>[[sklearn.tree]].<span style="font-weight:italic; color:Green">Model_Name()</i></code> <P>where <i>Model_Name</i> is the name of the selected [[K-Nearest Neighbor System]].
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*** <code>[[sklearn.neighbors Module|sklearn.neighbors]].<span style="font-weight:italic; color:Green">Model_Name(self, arguments)</i></code>  or simply <code>[[sklearn.tree]].<span style="font-weight:italic; color:Green">Model_Name()</i></code> <P>where <i>Model_Name</i> is the name of the selected [[K-Nearest Neighbor System]].
 
*** It can cotain [[Unsupervised kNN Learning System]]s, [[Supervised kNN Classification System]]s and [[Supervised kNN Regression System]]s.
 
*** It can cotain [[Unsupervised kNN Learning System]]s, [[Supervised kNN Classification System]]s and [[Supervised kNN Regression System]]s.
 
* <B>Example(s)</B>:  
 
* <B>Example(s)</B>:  
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=== 2016 ===
 
=== 2016 ===
 
* (Scikit-Learn, 2016) &rArr; "1.6. Nearest Neighbors" http://scikit-learn.org/stable/modules/neighbors.html
 
* (Scikit-Learn, 2016) &rArr; "1.6. Nearest Neighbors" http://scikit-learn.org/stable/modules/neighbors.html
** QUOTE: [[sklearn.neighbors]] provides functionality for [[unsupervised neighbors-based learning method|unsupervised]] and [[supervised neighbors-based learning method]]s. </s> ...        <P>        ... The classes in [[sklearn.neighbors]] can handle either [[Numpy array]]s or [[scipy.sparse matrice]]s as input. </s> For [[dense matrice]]s, a large number of possible [[distance metric]]s are supported. </s> For [[sparse matrice]]s, [[arbitrary Minkowski metric]]s are supported for [[search]]es. </s> There are many [[learning routine]]s which rely on [[nearest neighbor]]s at their core. </s> One example is [[kernel density estimation]], discussed in the [[density estimation]] section. </s>
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** QUOTE: [[sklearn.neighbors Module|sklearn.neighbors]] provides functionality for [[unsupervised neighbors-based learning method|unsupervised]] and [[supervised neighbors-based learning method]]s. </s> ...        <P>        ... The classes in [[sklearn.neighbors Module|sklearn.neighbors]] can handle either [[Numpy array]]s or [[scipy.sparse matrice]]s as input. </s> For [[dense matrice]]s, a large number of possible [[distance metric]]s are supported. </s> For [[sparse matrice]]s, [[arbitrary Minkowski metric]]s are supported for [[search]]es. </s> There are many [[learning routine]]s which rely on [[nearest neighbor]]s at their core. </s> One example is [[kernel density estimation]], discussed in the [[density estimation]] section. </s>
  
 
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__NOTOC__
 
__NOTOC__
 
[[Category:Concept]]
 
[[Category:Concept]]

Latest revision as of 20:45, 23 December 2019

An sklearn.neighbors Module is a nearest neighbors system within sklearn.



References

  • (Scikit-Learn, 2017) ⇒ "sklearn.neighbors: Nearest Neighbors" http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors Retrieved: 2017-11-12
    • QUOTE: The sklearn.neighbors module implements the k-nearest neighbors algorithm.

      User guide: See the Nearest Neighbors section for further details.

      • neighbors.BallTree BallTree for fast generalized N-point problems
      • neighbors.DistanceMetric DistanceMetric class
      • neighbors.KDTree KDTree for fast generalized N-point problems
      • neighbors.KernelDensity([bandwidth, …]) Kernel Density Estimation
      • neighbors.KNeighborsClassifier([…]) Classifier implementing the k-nearest neighbors vote.
      • neighbors.KNeighborsRegressor([n_neighbors, …]) Regression based on k-nearest neighbors.
      • neighbors.LocalOutlierFactor([n_neighbors, …]) Unsupervised Outlier Detection using Local Outlier Factor (LOF)
      • neighbors.RadiusNeighborsClassifier([…]) Classifier implementing a vote among neighbors within a given radius
      • neighbors.RadiusNeighborsRegressor([radius, …]) Regression based on neighbors within a fixed radius.
      • neighbors.NearestCentroid([metric, …]) Nearest centroid classifier.
      • neighbors.NearestNeighbors([n_neighbors, …]) Unsupervised learner for implementing neighbor searches.
      • neighbors.kneighbors_graph(X, n_neighbors[, …]) Computes the (weighted) graph of k-Neighbors for points in X
      • neighbors.radius_neighbors_graph(X, radius) Computes the (weighted) graph of Neighbors for points in X

2016