What is confusion matrix
Confusion matrix is typically used to compare the performance of a model. For example, when you do KNN on the iris data set, you can use the confusion matrix to compare the real values vs the predicted values.
# Split the data into train and test set.
require(caret)
set.seed(100)
# Get the split indices
trainIndex <- createDataPartition(iris$Species, p = .8,
list = FALSE,
times = 1)
# Generate the training and test data
train = iris[trainIndex,]
test = iris[-trainIndex,]
# The "class" package
require(class)
predict = knn(train = train[,1:4],
test = test[,1:4],
cl = train[,5],
k = 5)
table(predict,test[,5])
predict setosa versicolor virginica
setosa 10 0 0
versicolor 0 8 1
virginica 0 2 9
What do you see in the confusion matrix ?
10 out of 10 times, “setosa” species was correctly predicted. 8 out of 9 times “versicolor” was correctly predicted. 9 out of 11 times, “virginica” was correctly predicted.