predict.pcccd_classifier {rcccd}

Pure and Proper Class Cover Catch Digraph Prediction

Description

predict.pcccd_classifier makes prediction using pcccd_classifier object.

Usage

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

Arguments

object

a pcccd_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.

...

not used.

Details

Estimations are based on nearest dominant neighbor in radius unit.

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_pcccd <- pcccd_classifier(x = x_train, y = y_train)
pred <- predict(object = m_pcccd, newdata = x_test)

# confusion matrix
table(y_test, pred)

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