Afronding en review

Unsupervised learning in R

Hank Roark

Senior Data Scientist at Boeing

Case study: afronding

Volledig data-analyseproces met unsupervised learning
Creatieve aanpak voor modelleren
Klaar voor echte vraagstukken

Soorten clustering

k-means vs. dendrogram

Dimensiereductie

PCA-projectie

Modelkeuze

# Initialize total within sum of squares error: wss
wss <- 0

# Look over 1 to 15 possible clusters
for (i in 1:15) {
  # Fit the model: km.out
  km.out <- kmeans(pokemon, centers = i, nstart = 20, iter.max = 50)
  # Save the within cluster sum of squares
  wss[i] <- km.out$tot.withinss
}

# Produce a scree plot
plot(1:15, wss, type = "b", 
     xlab = "Number of Clusters", 
     ylab = "Within groups sum of squares")

PCA-resultaten interpreteren

biplot en screeplot

Belang van schalen

vergelijking van geschaalde en ongeschaalde feature-importance

Cursusreview

pr.iris <- prcomp(x = iris[-5],
                  scale = FALSE,
                  center = TRUE)
summary(pr.iris)

Importance of components:
                          PC1     PC2    PC3     PC4
Standard deviation     2.0563 0.49262 0.2797 0.15439
Proportion of Variance 0.9246 0.05307 0.0171 0.00521
Cumulative Proportion  0.9246 0.97769 0.9948 1.00000

Dendrogram

dendrogram

Sterke en zwakke punten per algoritme

verschillende clusteringresultaten

Cursusreview

# Repeat for components 1 and 3
plot(wisc.pr$x[, c(1, 3)], col = (diagnosis + 1), 
     xlab = "PC1", ylab = "PC3")

hoofcomponenten

Laten we oefenen!

Unsupervised learning in R