Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
|
| gptkbp:advantage |
simple to implement
computationally expensive at prediction no training phase sensitive to irrelevant features sensitive to the scale of data |
| gptkbp:alternativeTo |
gptkb:tree
gptkb:naive_Bayes support vector machine logistic regression |
| gptkbp:category |
instance-based learning
lazy learning |
| gptkbp:compatibleWith |
model training
|
| gptkbp:dependsOn |
gptkb:Metric
feature scaling choice of k |
| gptkbp:fullName |
K-Nearest Neighbors
|
| gptkbp:implementedIn |
gptkb:MATLAB
gptkb:Weka 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:output |
class label
regression value |
| gptkbp:parameter |
number of neighbors (k)
|
| gptkbp:relatedTo |
gptkb:Metric
gptkb:curse_of_dimensionality feature scaling majority voting weighted voting |
| gptkbp:requires |
labeled data
|
| gptkbp:type |
supervised learning
|
| gptkbp:usedFor |
gptkb:dictionary
regression |
| gptkbp:usedIn |
gptkb:machine_learning
image recognition pattern recognition recommendation systems data mining anomaly detection |
| gptkbp:bfsParent |
gptkb:KNN관객상
|
| gptkbp:bfsLayer |
7
|
| https://www.w3.org/2000/01/rdf-schema#label |
KNN
|