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spark.mllib: data types, algorithms, and utilities
Data types
Basic statistics
summary statistics
correlations
stratified sampling
hypothesis testing
streaming significance testing
random data generation
Classification and regression
linear models (SVMs, logistic regression, linear regression)
naive Bayes
decision trees
ensembles of trees (Random Forests and Gradient-Boosted Trees)
isotonic regression
Collaborative filtering
alternating least squares (ALS)
Clustering
k-means
Gaussian mixture
power iteration clustering (PIC)
latent Dirichlet allocation (LDA)
bisecting k-means
streaming k-means
Dimensionality reduction
singular value decomposition (SVD)
principal component analysis (PCA)
Feature extraction and transformation
Frequent pattern mining
FP-growth
association rules
PrefixSpan
Evaluation metrics
PMML model export
Optimization (developer)
stochastic gradient descent
limited-memory BFGS (L-BFGS)
spark.ml: high-level APIs for ML pipelines
Overview: estimators, transformers and pipelines
Extracting, transforming and selecting features
Classification and regression
Clustering
Advanced topics