Algorithm Portfolio Selection with Machine Learning
Create DALEX explainers for multiple ASML-trained models
Predicting the KPI value for the algorithms
Internal generic for ASpredict
Training models for posterior selection of algorithms
Internal generic for AStrain
Boxplots
Internal generic for boxplots
Internal generic for figure_comparison
KPI summary table
Internal generic for KPI_summary_table
KPI table
Internal generic for KPI_table
Machine learning process
Partition and Normalize
Plot
Ranking Plot
Internal generic for ranking
Figure Comparison
A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).