R-universe search: topic:mlr3

mlr-org

mlr3:Machine Learning in R - Next Generation

Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.

Maintained by Marc Becker. Last updated 18 days ago.

classification data-science machine-learning mlr3 regression

972 stars 14.86 score 2.3k scripts 35 dependents

mlr-org

mlr3pipelines:Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

Maintained by Martin Binder. Last updated 22 days ago.

bagging data-science dataflow-programming ensemble-learning machine-learning mlr3 pipelines preprocessing stacking

141 stars 12.36 score 448 scripts 7 dependents

mlr-org

mlr3learners:Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

Maintained by Marc Becker. Last updated 10 days ago.

classification learners machine-learning mlr3 regression

91 stars 11.57 score 1.5k scripts 11 dependents

mlr-org

paradox:Define and Work with Parameter Spaces for Complex Algorithms

Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.

Maintained by Martin Binder. Last updated 9 months ago.

experimental-design hyperparameters mlr3 transformations

29 stars 11.56 score 316 scripts 38 dependents

mlr-org

mlr3tuning:Hyperparameter Optimization for 'mlr3'

Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.

Maintained by Marc Becker. Last updated 3 months ago.

bbotk hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 optimization tune tuning

55 stars 11.53 score 384 scripts 11 dependents

mlr-org

mlr3misc:Helper Functions for 'mlr3'

Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.

Maintained by Marc Becker. Last updated 4 months ago.

machine-learning miscellaneous mlr3

12 stars 10.28 score 302 scripts 42 dependents

mlr-org

bbotk:Black-Box Optimization Toolkit

Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'.

Maintained by Marc Becker. Last updated 4 months ago.

bbotk black-box-optimization data-science hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 optimization

22 stars 9.83 score 166 scripts 14 dependents

mlr-org

mlr3viz:Visualizations for 'mlr3'

Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.

Maintained by Marc Becker. Last updated 5 months ago.

ggplot2 mlr3 visualization visualizations

45 stars 9.45 score 364 scripts 4 dependents

doubleml

DoubleML:Double Machine Learning in R

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Maintained by Philipp Bach. Last updated 4 months ago.

causal-inference data-science double-machine-learning econometrics machine-learning mlr3 statistics

139 stars 9.16 score 267 scripts 1 dependents

mlr-org

mlr3extralearners:Extra Learners For mlr3

Extra learners for use in mlr3.

Maintained by Sebastian Fischer. Last updated 5 months ago.

machine-learning mlr3

94 stars 9.16 score 474 scripts

mlr-org

mlr3mbo:Flexible Bayesian Optimization

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

Maintained by Lennart Schneider. Last updated 26 days ago.

automl bayesian-optimization bbotk black-box-optimization gaussian-process hpo hyperparameter hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 model-based-optimization optimization optimizer random-forest tuning

25 stars 8.57 score 120 scripts 3 dependents

mlr-org

mlr3filters:Filter Based Feature Selection for 'mlr3'

Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.

Maintained by Marc Becker. Last updated 5 months ago.

feature-selection filter filters mlr mlr3 variable-importance

20 stars 8.37 score 95 scripts 3 dependents

mlr-org

mlr3verse:Easily Install and Load the 'mlr3' Package Family

The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.

Maintained by Marc Becker. Last updated 2 months ago.

machine-learning meta mlr3

55 stars 8.32 score 720 scripts 1 dependents

mlr-org

mlr3cluster:Cluster Extension for 'mlr3'

Extends the 'mlr3' package with cluster analysis.

Maintained by Maximilian Mücke. Last updated 1 months ago.

cluster-analysis clustering mlr3

23 stars 8.31 score 50 scripts 2 dependents

mlr-org

mlr3fselect:Feature Selection for 'mlr3'

Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

Maintained by Marc Becker. Last updated 2 months ago.

evolutionary-algorithms exhaustive-search feature-selection machine-learning mlr3 optimization random-search recursive-feature-elimination sequential-feature-selection

23 stars 8.12 score 70 scripts 2 dependents

mlr-org

mlr3spatiotempcv:Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

Maintained by Patrick Schratz. Last updated 4 months ago.

cross-validation mlr3 resampling resampling-methods spatial temporal

50 stars 8.09 score 123 scripts

mlr-org

mlr3proba:Probabilistic Supervised Learning for 'mlr3'

Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures.

Maintained by John Zobolas. Last updated 3 months ago.

density-estimation machine-learning mlr3 probabilistic-regression probabilistic-supervised-learning supervised-learning survival-analysis cpp

135 stars 7.78 score 246 scripts

mlr-org

mlr3torch:Deep Learning with 'mlr3'

Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'.

Maintained by Sebastian Fischer. Last updated 1 months ago.

data-science deep-learning machine-learning mlr3 torch

42 stars 7.63 score 78 scripts

mlr-org

mlr3hyperband:Hyperband for 'mlr3'

Successive Halving (Jamieson and Talwalkar (2016) <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018 <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

Maintained by Marc Becker. Last updated 9 months ago.

automl bbotk hyperband hyperparameter-tuning machine-learning mlr3 optimization tune tuning

18 stars 7.36 score 44 scripts 3 dependents

mlr-org

mlr3spatial:Support for Spatial Objects Within the 'mlr3' Ecosystem

Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'.

Maintained by Marc Becker. Last updated 1 years ago.

mlr3 raster-prediction spatial spatial-modelling

43 stars 6.75 score 66 scripts

mlr-org

mlr3oml:Connector Between 'mlr3' and 'OpenML'

Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package.

Maintained by Sebastian Fischer. Last updated 10 months ago.

data data-science datasets machine-learning mlr3 openml cpp

7 stars 5.37 score 105 scripts

mlr-org

mlr3data:Collection of Machine Learning Data Sets for 'mlr3'

A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (<https://mlr3book.mlr-org.com>), the use case gallery (<https://mlr3gallery.mlr-org.com>), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded.

Maintained by Marc Becker. Last updated 5 months ago.

data data-science data-sets machine-learning mlr3

2 stars 5.28 score 18 scripts 2 dependents

lamate

mlr3shiny:Machine Learning in 'shiny' with 'mlr3'

A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.

Maintained by Laurens Tetzlaff. Last updated 10 months ago.

data-science introduction-to-machine-learning machine-learning mlr3 shiny supervised-learning

28 stars 5.23 score 1 scripts

mlr-org

mlr3benchmark:Analysis and Visualisation of Benchmark Experiments

Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond.

Maintained by Sebastian Fischer. Last updated 2 years ago.

analysis benchmark-analysis benchmark-experiments benchmarking mlr3

12 stars 5.18 score 50 scripts

mlr-org

mlr3fda:Extending 'mlr3' to Functional Data Analysis

Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'.

Maintained by Sebastian Fischer. Last updated 8 months ago.

data-analysis data-analysis-in-r data-science functional-data machine-learning mlr3

5 stars 4.95 score 5 scripts

mlr-org

rush:Rapid Parallel and Distributed Computing

Parallel computing with a network of local and remote workers. Fast exchange of results between the workers through a 'Redis' database. Key features include task queues, local caching, and sophisticated error handling.

Maintained by Marc Becker. Last updated 5 months ago.

mlr3 parallel-computing

11 stars 4.94 score 5 scripts

mlr-org

mlr3batchmark:Batch Experiments for 'mlr3'

Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

Maintained by Marc Becker. Last updated 1 years ago.

batchtools cluster-computing high-performance-computing hpc mlr3

5 stars 4.85 score 57 scripts

mlr-org

mlr3db:Data Base Backend for 'mlr3'

Extends the 'mlr3' package with a backend to transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or 'PostgreSQL'. The package provides two additional backends: 'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases and also on Apache Parquet files.

Maintained by Michel Lang. Last updated 1 years ago.

bigquery data-backend database duckdb machine-learning mariadb mlr3 mysql odbc postgresql spark sqlite

21 stars 4.77 score 17 scripts