THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
Potential roadmap items 2017+:
Predictive models
Novelty detection using 1-class SVM MADLIB-990- Mixed effects modeling MADLIB-987
- Factorization machines
k-nearest neighbors (kNN) MADLIB-927- Geographically Weighted Regression (GWR) MADLIB-1023
- MCMC Probit and Logit regression
- Gaussian Mixture Model using Expectation Maximization (EM) algorithm MADLIB-410
- Multi-layer Perceptron
- Hierarchical clustering
- Neural nets and deep learning
Graph
Single source shortest path MADLIB-992- All pairs shortest path
- One mode projection (converting a bi-partitite graph of user-item graph to user-user or item-item graph)
- Connected components
- Page rank
- Graph cut
- Centrality measures like betweenness and closeness
- Triangle counting
- Minimum spanning tree
- Graph diameter
Utilities
Path functions (phase 2) MADLIB-977Prediction metrics MADLIB-907Sessionization MADLIB-909Pivoting MADLIB-908- Anonymization MADLIB-911
- URI tools MADLIB-910
- Stratified sampling MADLIB-986
Usability
- Expand coverage for PivotalR
- Expand coverage for PMML export??? (or perhaps switch to PFA???)
- Interface improvement and consistency for 2.0 release (does not need to be backward compatible)
- Implement an interface using named parameters
- Python API
Performance and scalability
- Work around PostgreSQL 1 GB field size limit MADLIB-991
- GPU support
- Improve decision tree and random forest performance for run-time and memory usage
Platform
Support for PostgreSQL 9.5 and 9.6 MADLIB-944- Tensorflow support, or another deep learning framework