Man pages for llama
Leveraging Learning to Automatically Manage Algorithms
| analysis | Analysis functions |
| bsFolds | Bootstrapping folds |
| classify | Classification model |
| classifyPairs | Classification model for pairs of algorithms |
| cluster | Cluster model |
| cvFolds | Cross-validation folds |
| helpers | Helpers |
| imputeCensored | Impute censored values |
| input | Read data |
| llama-package | Leveraging Learning to Automatically Manage Algorithms |
| misc | Convenience functions |
| misclassificationPenalties | Misclassification penalty |
| normalize | Normalize features |
| parscores | Penalized average runtime score |
| plot | Plot convenience functions to visualise selectors |
| regression | Regression model |
| regressionPairs | Regression model for pairs of algorithms |
| satsolvers | Example data for Leveraging Learning to Automatically Manage... |
| successes | Success |
| trainTest | Train / test split |
| tune | Tune the hyperparameters of the machine learning algorithm... |
