Spark MLlib

Data types

Basic statistics

summary statistics
correlations
stratified sampling
hypothesis testing
streaming significance testing
random data generation

Classification and regressionlinear models (SVMs, logistic regression, linear regression)
naive Bayes
decision trees
ensembles of trees (Random Forests and Gradient-Boosted Trees)
isotonic regression

Collaborative filteringalternating least squares (ALS)

Clusteringk-means
Gaussian mixture
power iteration clustering (PIC)
latent Dirichlet allocation (LDA)
bisecting k-means
streaming k-means

Dimensionality reductionsingular value decomposition (SVD)
principal component analysis (PCA)

Feature extraction and transformation
Frequent pattern miningFP-growth
association rules
PrefixSpan

Evaluation metrics
PMML model export
Optimization (developer)stochastic gradient descent
limited-memory BFGS (L-BFGS)