classifying D&D monsters with isomap and k-means
unsupervised learning algorithms such as Isomap and K-means can produce intuitive categorizations for unlabeled data
unsupervised learning algorithms such as Isomap and K-means can produce intuitive categorizations for unlabeled data
we can use distance-preserving maps to simplify and get a useful overview of our data
by rotating a noisy line we can better understand the meaning of principal component decomposition
we perform principal component analysis on song data from 1950 to 2010 and interpret the transformed variables
we use random forest decision tree aggregation to study the gender wage gap in the US from 2012