R-universe search: topic:hyperparameter-optimization

Showing 5 of total 5 results (show query)

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

facebookexperimental

Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Maintained by Gufeng Zhou. Last updated 11 days ago.

adstocking budget-allocation cost-response-curve econometrics evolutionary-algorithm gradient-based-optimisation hyperparameter-optimization marketing-mix-modeling marketing-mix-modelling marketing-science mmm ridge-regression

1.3k stars 10.27 score 95 scripts

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

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

kapsner

mlexperiments:Machine Learning Experiments

Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'ParBayesianOptimization' <https://cran.r-project.org/package=ParBayesianOptimization>) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class.

Maintained by Lorenz A. Kapsner. Last updated 25 days ago.

cross-validation experiment hyperparameter-optimization hyperparameter-tuning machine-learning nested

5 stars 7.64 score 49 scripts 2 dependents