Nearest Centroid Classifier
In this notebook, we will investigate how to use the nearest centroid classifier.
We simulate 400 examples of 30 dimensional points:
testdat <- lol.sims.mean_diff(n, d)
X <- testdat$X
Y <- testdat$Y
data <- data.frame(x1=X[,1], x2=X[,2], y=Y)
data$y <- factor(data$y)
ggplot(data, aes(x=x1, y=x2, color=y)) +
geom_point() +
xlab("x1") +
ylab("x2") +
ggtitle("Simulated Data") +
xlim(-4, 6) +
ylim(-4, 4)
We estimate the centers with the nearestCentroid classifier:
classifier <- lol.classify.nearestCentroid(X, Y)
data <- cbind(data, data.frame(size=1))
data <- rbind(data, data.frame(x1=classifier$centroids[,1], x2=classifier$centroids[,2], y="center", size=5))
ggplot(data, aes(x=x1, y=x2, color=y, size=size)) +
geom_point() +
xlab("x1") +
ylab("x2") +
ggtitle("Data with estimated Centers") +
guides(size=FALSE) +
xlim(-4, 6) +
ylim(-4, 4)
Yhat <- predict(classifier, X)
data$y[1:(length(data$y) - 2)] <- Yhat
ggplot(data, aes(x=x1, y=x2, color=y, size=size)) +
geom_point() +
xlab("x1") +
ylab("x2") +
ggtitle("Data with Predictions") +
guides(size=FALSE) +
xlim(-4, 6) +
ylim(-4, 4)