About Spark MLlib

MLlib works with Spark's APIs and with NumPy in Python and with R libraries. Since Spark excels at iterative computation, MLlib runs very fast with highly-scalable, high-quality algorithms that leverage iteration.

Included Functionality:

ML algorithms include:

• Classification: logistic regression, naive Bayes,...

• Regression: generalized linear regression, survival regression,...

• Decision trees, random forests, and gradient-boosted trees

• Recommendation: alternating least squares (ALS)

• Clustering: K-means, Gaussian mixtures (GMMs),...

• Topic modeling: latent Dirichlet allocation (LDA)

• Frequent itemsets, association rules, and sequential pattern mining

ML workflow utilities include:

• Feature transformations: standardization, normalization, hashing,...

• ML Pipeline construction

• Model evaluation and hyper-parameter tuning

• ML persistence: saving and loading models and Pipelines

Other utilities include:

• Distributed linear algebra: SVD, PCA,...

• Statistics: summary statistics, hypothesis testing,...

Resources

• ​Spark MLlib Website​

• ​Getting Starting Guide​