llama v0.9.2
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Leveraging Learning to Automatically Manage Algorithms
Provides functionality to train and evaluate algorithm selection models for portfolios.
Functions in llama
| Name | Description | |
| satsolvers | Example data for Leveraging Learning to Automatically Manage Algorithms | |
| tune | Tune the hyperparameters of the machine learning algorithm underlying a model | |
| normalize | Normalize features | |
| imputeCensored | Impute censored values | |
| llama-package | Leveraging Learning to Automatically Manage Algorithms | |
| classifyPairs | Classification model for pairs of algorithms | |
| bsFolds | Bootstrapping folds | |
| classify | Classification model | |
| analysis | Analysis functions | |
| input | Read data | |
| cvFolds | Cross-validation folds | |
| cluster | Cluster model | |
| helpers | Helpers | |
| trainTest | Train / test split | |
| misc | Convenience functions | |
| successes | Success | |
| plot | Plot convenience functions to visualise selectors | |
| regressionPairs | Regression model for pairs of algorithms | |
| misclassificationPenalties | Misclassification penalty | |
| parscores | Penalized average runtime score | |
| regression | Regression model | |
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Details
| Type | Package |
| Date | 2018-07-11 |
| License | BSD_3_clause + file LICENSE |
| URL | https://bitbucket.org/lkotthoff/llama |
| NeedsCompilation | no |
| Packaged | 2018-07-11 10:43:46 UTC; larsko |
| Repository | CRAN |
| Date/Publication | 2018-07-11 14:30:03 UTC |
| imports | BBmisc , checkmate , ggplot2 , parallelMap , plyr , rJava |
| depends | mlr (>= 2.5) , R (>= 2.10) |
| suggests | ParamHelpers , testthat |
| Contributors | Barry Hurley, Talal Rahwan, Lars Kotthoff, Bernd Bischl |
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