MADlib v1.7.1

Release Date: 2015-March-18

New features:

• Random Forest Performance Improvement
  • Function forest_train() is 1.5X ~ 4X faster without variable importance,
    and up to 100X faster with variable importance
  • Function forest_predict() is up to 10X faster when type='response'
  • Allow user-specified sample ratio to train with a small subsample
• Gaussian Naive Bayes: allow continuous variables
• K-Means: Allow user-specified sample ratio for K-means++ seeding
• Miscellaneous
  • Array functions: array_square() for element-wise square, madlib.sum()
    for array element-wise aggregation
  • Madpack does not require password when not necessary (MADLIB-357)
  • Platform support of PostgreSQL 9.4 and HAWQ 1.3
  • Allow views and materialized views for training functions
  • Support quantile computation in summary functions for HAWQ and PG 9.4

Bug fixes:

• Fixed the support of multiple parameter values and NULL in general
  cross-validation (MADLIB-898, MADLIB-896)
• Fixed infinite loop when detecting recursive view-to-view dependencies for
  upgrading (MADLIB-901)
• Allow user-specified column names in PCA and multinom_predict()

Known issues:

• Performance for decision tree with cross-validation is poor on a HAWQ
  multi-node system.