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mlr - mlr: Machine Learning in R

  •    R

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

mlens - ML-Ensemble – high performance ensemble learning

  •    Python

ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible. ML-Ensemble is thread safe as long as base learners are and can fall back on memory mapped multiprocessing for memory-neutral process-based concurrency. For tutorials and full documentation, visit the project website.

beginner-projects - 👶 A list of projects for beginners.


A list of projects for beginners. If you're completely new to programming, you may want to look into a tutorial. Before starting, you should already know the basics of programming such as variables, loops, dictionaries, and how to define functions, as well as how to run programs on your own machine.

graph-pattern-learner - Evolutionary Graph Pattern Learner that learns SPARQL queries for a given set of source-target-pairs from an endpoint

  •    Python

In this repository you find the code for a graph pattern learner. Given a list of source-target-pairs and a SPARQL endpoint, it will try to learn SPARQL patterns. Given a source, the learned patterns will try to lead you to the right target. As you can immediately see, associations don't only follow a single pattern. Our algorithm is designed to be able to deal with this. It will try to learn several patterns, which in combination model your input list of source-target-pairs. If your list of source-target-pairs is less complicated, the algorithm will happily terminate earlier.

mlrHyperopt - Easy Hyper Parameter Optimization with mlr and mlrMBO.

  •    HTML

Easy Hyper Parameter Optimization with mlr and mlrMBO. Mainly it uses the learner implemented in mlr and uses the tuning methods also available in mlr. Unfortunately mlr lacks of well defined search spaces for each learner to make hyperparameter tuning easy.