...

The roadmap below can provide an indication of the algorithms we aim to implement in for the coming
months. If you are interested in helping out, please check our [contribution guide](contribution_guide.html).library.
Items in bold have already been implemented:

• Pipelines of transformers and learners
• Data pre-processing
  • Feature scaling
  • Polynomial feature base mapper
  • Feature hashing
  • Feature extraction for text
  • Dimensionality reduction
• Model selection and performance evaluation
  • Model evaluation using a variety of scoring functions
  • Cross-validation for model selection and evaluation
  • Hyper-parameter optimization
• Supervised learning
  • Optimization framework
    • Stochastic Gradient Descent
    • L-BFGS
  • Generalized Linear Models
    • Multiple linear regression
    • LASSO, Ridge regression
    • Multi-class Logistic regression
    • Random forests
    • Support Vector Machines
  • Decision trees
• Unsupervised learning
  • Clustering
    • K-means clustering
  • Principal Components Analysis
• Recommendation
  • ALS
• Text analytics
  • LDA
• Statistical estimation tools
• Distributed linear algebra
• Streaming ML

How can I help?

Any contribution to the above roadmap is welcome! You can also check out the list of open issues
for FlinkML on JIRA, or send a message with your idea to the Flink developers list.

We recommend reading the FlinkML contribution guide before starting out, and definitely
subscribe and post a message on the Flink developers mailing list to introduce yourself!

References: