hanaml.KNNClassifier {hana.ml.r} | R Documentation |
hanaml.KNNClassifier is an R wrapper for PAL KNN algorithm for classification.
hanaml.KNNClassifier(conn.context, data = NULL, key = NULL, features = NULL, label = NULL, n.neighbors = NULL, voting.type = NULL, metric = NULL, minkowski.power = NULL, algorithm = NULL, categorical.variable = NULL)
conn.context |
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data |
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key |
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features |
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label |
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n.neighbors |
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voting.type |
Defaults to 'distance-weighted'. Defaults to TRUE. |
metric |
Defaults to 'euclidean'. |
minkowski.power |
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algorithm |
Defaults to 'brute-force'. |
categorical.variable |
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R6Class
object.
K-Nearest Neighbor (KNN) is a memory-based classification or regression method with no explicit training phase. For classificatin purpose, it assumes that instances should have similar labels to their nearest neighbors.
A "KNNClassifier" object.
## Not run: Training data: > df.train$Collect() ID X1 X2 X3 TYPE 1 0 2 1 A 1 2 1 3 10 A 10 3 2 3 10 B 10 4 3 3 10 C 1 5 4 1 1000 C 1 6 5 1 1000 A 10 7 6 1 1000 B 99 8 7 1 999 A 99 9 8 1 999 B 10 10 9 1 1000 C 10 knc <- hanaml.KNNClassifier(conn.context = conn, data = df.train, key = "ID", features = c("X1", "X2", "X3"), label = "TYPE", n.neighbors = 3, voting.type = "majority", algorithm = "brute-force", categorical.variable = c("X1")) ## End(Not run)