classLoss: Classification Performance Measure

In davpinto/fastknn: Build Fast k-Nearest Neighbor Classifiers

Description

Compute classification performance according to common evaluation metrics: classification error, AUC and log-loss.

Usage

classLoss(actual, predicted, prob, eval.metric = "overall_error")

Arguments

actual	factor array with the true class labels.
predicted	factor array with the predicted class labels.
prob	matrix with predicted class membership probabilities. Rows are observations and columns are classes. It is required to calculate AUC and log-loss.
eval.metric	evaluation metric to be used. It can be one of c("overall_error", "mean_error", "auc", "logloss"). The default option is "overall_error".

Details

There are four evaluation metrics available sor far:

• eval.metric="overal_error": default option. It gives the overall misclassification rate. It do not require the prob parameter.

• eval.metric="mean_error": gives the mean per class misclassification rate. It do not require the prob parameter.

• eval.metric="auc": gives the mean per class area under the ROC curve. It requires the prob parameter.

• eval.metric="logloss": gives the cross-entropy or logarithmic loss. It requires the prob parameter.

Value

The classification performance measure.

Author(s)

David Pinto.

Examples

## Not run: 
library("mlbench")
library("caTools")
library("fastknn")

data("Ionosphere")

x <- data.matrix(subset(Ionosphere, select = -Class))
y <- Ionosphere$Class

set.seed(2048)
tr.idx <- which(sample.split(Y = y, SplitRatio = 0.7))
x.tr <- x[tr.idx,]
x.te <- x[-tr.idx,]
y.tr <- y[tr.idx]
y.te <- y[-tr.idx]

knn.out <- fastknn(xtr = x.tr, ytr = y.tr, xte = x.te, k = 10)

classLoss(actual = y.te, predicted = knn.out$class, eval.metric = "overall_error")
classLoss(actual = y.te, predicted = knn.out$class, prob = knn.out$prob, eval.metric = "logloss")

## End(Not run)

davpinto/fastknn documentation built on May 15, 2019, 1:18 a.m.