Statements (43)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
|
| gptkbp:advantage |
simple to implement
no training phase sensitive to irrelevant features sensitive to the scale of data computationally expensive at prediction time |
| gptkbp:alternativeName |
gptkb:k-NN
k nearest neighbors |
| gptkbp:category |
instance-based learning
lazy learning |
| gptkbp:decision |
majority vote
average of neighbors (for regression) |
| https://www.w3.org/2000/01/rdf-schema#label |
KNN (K-Nearest Neighbors)
|
| gptkbp:implementedIn |
gptkb:MATLAB
gptkb:scikit-learn R |
| gptkbp:input |
feature vectors
|
| gptkbp:introduced |
Fix and Hodges
|
| gptkbp:introducedIn |
1951
|
| gptkbp:measures |
gptkb:Hamming_distance
gptkb:Manhattan_distance gptkb:Minkowski_distance Euclidean distance |
| gptkbp:notableFor |
pattern recognition
recommendation systems data mining image classification text categorization |
| gptkbp:output |
class label
regression value |
| gptkbp:parameter |
number of neighbors (k)
|
| gptkbp:preprocessing |
normalization
feature scaling |
| gptkbp:relatedTo |
gptkb:tree
support vector machine nearest centroid classifier |
| gptkbp:requires |
labeled data
|
| gptkbp:sensitivity |
gptkb:curse_of_dimensionality
|
| gptkbp:type |
supervised learning
|
| gptkbp:usedFor |
gptkb:dictionary
regression |
| gptkbp:bfsParent |
gptkb:HNSW
|
| gptkbp:bfsLayer |
7
|