results {Kira}

Results of the classification process

Description

Returns the results of the classification process.

Usage

results(orig.class, predict)

Arguments

orig.class

Data with the original classes.

predict

Data with classes of results of classifiers.

Value

mse

Mean squared error.

mae

Mean absolute error.

rae

Relative absolute error.

conf.mtx

Confusion matrix.

rate.hits

Hit rate.

rate.error

Error rate.

num.hits

Number of correct instances.

num.error

Number of wrong instances.

kappa

Kappa coefficient.

roc.curve

Data for the ROC curve in classes.

prc.curve

Data for the PRC curve in classes.

res.class

General results of the classes: Sensitivity, Specificity, Precision, TP Rate, FP Rate, NP Rate, F-Score, MCC, ROC Area, PRC Area.

Author(s)

Paulo Cesar Ossani

References

Chicco, D.; Warrens, M. J. and Jurman, G. The matthews correlation coefficient (mcc) is more informative than cohen's kappa and brier score in binary classification assessment. IEEE Access, IEEE, v. 9, p. 78368-78381, 2021.

See Also

plot_curve

Examples

data(iris) # data set

data  <- iris
names <- colnames(data)
colnames(data) <- c(names[1:4],"class")

#### Start - hold out validation method ####
dat.sample = sample(2, nrow(data), replace = TRUE, prob = c(0.7,0.3))
data.train = data[dat.sample == 1,] # training data set
data.test  = data[dat.sample == 2,] # test data set
class.train = as.factor(data.train$class) # class names of the training data set
class.test  = as.factor(data.test$class)  # class names of the test data set
#### End - hold out validation method ####


dist = "euclidean" 
# dist = "manhattan"
# dist = "minkowski"
# dist = "canberra"
# dist = "maximum"
# dist = "chebyshev"

k = 1
lambda = 5

r <- (ncol(data) - 1)
res <- knn(train = data.train[,1:r], test = data.test[,1:r], class = class.train, 
           k = 1, dist = dist, lambda = lambda)

resp <- results(orig.class = class.test, predict = res$predict)

message("Mean squared error:"); resp$mse
message("Mean absolute error:"); resp$mae
message("Relative absolute error:"); resp$rae
message("Confusion matrix:"); resp$conf.mtx  
message("Hit rate: ", resp$rate.hits)
message("Error rate: ", resp$rate.error)
message("Number of correct instances: ", resp$num.hits)
message("Number of wrong instances: ", resp$num.error)
message("Kappa coefficient: ", resp$kappa)
# message("Data for the ROC curve in classes:"); resp$roc.curve 
# message("Data for the PRC curve in classes:"); resp$prc.curve
message("General results of the classes:"); resp$res.class

dat <- resp$roc.curve; tp = "roc"; ps = 3
# dat <- resp$prc.curve; tp = "prc"; ps = 4

plot_curve(data = dat, type = tp, title = NA, xlabel = NA, ylabel = NA,  
           posleg = ps, boxleg = FALSE, axis = TRUE, size = 1.1, grid = TRUE, 
           color = TRUE, classcolor = NA, savptc = FALSE, width = 3236, 
           height = 2000, res = 300, casc = FALSE)