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Some potential roadmap items for 2018+ below. For more details please see the JIRAs.
Predictive models
- Multi-class SVM
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- Mixed effects modeling
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- Gradient boosted machines
- Gaussian Mixture Model using Expectation Maximization (EM) algorithm
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- Hierarchical clustering
- Deep learning
- k-NN variations
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Graph
- Personalized PageRank
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- Betweenness centrality
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- Graph cut
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- Triangle counting
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- Minimum spanning tree
- Eigenvector centrality
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- APSP performance improvements
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Utilities
- Balanced datasets
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Summary - add more statistics
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- Anonymization
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- URI tools
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Usability
- Expand coverage for PivotalR
- Interface improvement and consistency for 2.0 release (does not need to be backward compatible)
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- Python API
- Mini-batching
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- GPU support
- Improve decision tree and random forest performance for run-time and memory usage
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- PostgreSQL 10 support
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- Tensorflow support, or another deep learning framework
- Support modern versions of gcc
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