mlr Files

Machine Learning in R

This is an exact mirror of the mlr project, hosted at https://github.com/mlr-org/mlr. SourceForge is not affiliated with mlr. For more information, see the SourceForge Open Source Mirror Directory.

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
Parent folder
mlr 2.19.0 source code.tar.gz	2021-02-23	47.8 MB	0
mlr 2.19.0 source code.zip	2021-02-23	48.4 MB	0
README.md	2021-02-23	686 Bytes	0
Totals: 3 Items		96.2 MB	0

Add filter FSelectoRcpp::relief(). This C++ based implementation of the RelieF filter algorithm is way faster than the Java based one from the {FSelector} package (#2804)
Fix S3 print method for FilterWrapper objects
Make ibrier measure work with survival tasks (#2789)
Switch to testthat v3 (#2796)
Enable parallel tests (#2796)
Replace package PMCMR by PMCMRplus (#2796)
Remove CoxBoost learner due to CRAN removal
Warning if fix.factors.prediction = TRUE causes the generation of NAs for new factor levels in prediction (@jakob-r, [#2794])
Clear error message if prediction of wrapped learner has not the same length as newdata (@jakob-r, [#2794])

Source: README.md, updated 2021-02-23

Other Useful Business Software

Auth0 for AI Agents now in GA Icon

Auth0 for AI Agents now in GA

Ready to implement AI with confidence (without sacrificing security)?

Connect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.

Start building today

Grafana: The open and composable observability platform Icon

Grafana: The open and composable observability platform

Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

Grafana is the open source analytics & monitoring solution for every database.

Learn More

Recommended Projects

Bootstrap.jl
Statistical bootstrapping library for Julia
Modular toolkit for Data Processing MDP
The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
Machine Learning Cheat Sheet
Classical equations and diagrams in machine learning
OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE)
ExplainableAI.jl
Explainable AI in Julia