predict.rwcccd_classifier {rcccd}

Random Walk Class Cover Catch Digraph Prediction

Description

predict.rwcccd_classifier makes prediction using rwcccd_classifier object.

Usage

## S3 method for class 'rwcccd_classifier'
predict(object, newdata, type = "pred", e = 0, ...)

Arguments

object

a rwcccd_classifier object

newdata

newdata as matrix or dataframe.

type

"pred" or "prob". Default is "pred". "pred" is class estimations, "prob" is n\times k matrix of class probabilities.

e

0 or 1. Default is 0. Penalty based on T scores in rwcccd_classifier object.

...

not used.

Details

Estimations are based on nearest dominant neighbor in radius unit. e argument is used to penalize estimations based on T scores in rwcccd_classifier object.

For detail, please refer to Priebe et al. (2001), Priebe et al. (2003), and Manukyan and Ceyhan (2016).

Value

a vector of class predictions (if type is "pred") or a n\times p matrix of class probabilities (if type is "prob").

Author(s)

Fatih Saglam, [e-mail]

References

Priebe, C. E., DeVinney, J., & Marchette, D. J. (2001). On the distribution of the domination number for random class cover catch digraphs. Statistics & Probability Letters, 55(3), 239–246. https://doi.org/10.1016/s0167-7152(01)00129-8

Priebe, C. E., Marchette, D. J., DeVinney, J., & Socolinsky, D. A. (2003). Classification Using Class Cover Catch Digraphs. Journal of Classification, 20(1), 3–23. https://doi.org/10.1007/s00357-003-0003-7

Manukyan, A., & Ceyhan, E. (2016). Classification of imbalanced data with a geometric digraph family. Journal of Machine Learning Research, 17(1), 6504–6543. https://jmlr.org/papers/volume17/15-604/15-604.pdf

Examples

n <- 1000
x1 <- runif(n, 1, 10)
x2 <- runif(n, 1, 10)
x <- cbind(x1, x2)
y <- as.factor(ifelse(3 < x1 & x1 < 7 & 3 < x2 & x2 < 7, "A", "B"))

# testing the performance
i_train <- sample(1:n, round(n*0.8))

x_train <- x[i_train,]
y_train <- y[i_train]

x_test <- x[-i_train,]
y_test <- y[-i_train]

m_rwcccd <- rwcccd_classifier(x = x_train, y = y_train)
pred <- predict(object = m_rwcccd, newdata = x_test, e = 0)

# confusion matrix
table(y_test, pred)

# test accuracy
sum(y_test == pred)/nrow(x_test)