Optimal Decision Trees for Nonlinear Metrics
Authors: Emir Demirović, Peter J. Stuckey3733-3741
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The value of our method is given in a dedicated experimental section, where we consider 75 publicly available datasets. Nevertheless, the experiments illustrate that runtimes are reasonable for majority of the tested datasets. |
| Researcher Affiliation | Academia | Delft University of Technology, The Netherlands Monash University and Data61, Australia |
| Pseudocode | Yes | Pseudo-code for the algorithm is given in Figure 1, where details on bounding the size of the tree in terms of numbers of nodes are elided for simplicity. |
| Open Source Code | Yes | Public release. The code and benchmarks are available at bitbucket.org/Emir D/murtree-bi-objective. |
| Open Datasets | Yes | We considered 75 binary classification datasets used in previous works (Verwer and Zhang 2019; Aglin, Nijssen, and Schaus 2020; Demirovic et al. 2020; Narodytska et al. 2018; Hu, Rudin, and Seltzer 2019). |
| Dataset Splits | Yes | Five-fold cross-validation is used to evaluate each combination of parameters and the parameters that maximises accuracy or F1-score on test set across the folds is selected. |
| Hardware Specification | Yes | The experiments were run one at a time on an Intel i7-3612QM@2.10 GHz with 8 GB RAM. |
| Software Dependencies | No | The paper mentions using the baseline algorithm Mur Tree (Demirovic et al. 2020) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We perform hyper-parameter tuning considering parameters depth {1, 2, 3, 4} and size {1, 2, ..., 2depth 1}. Five-fold cross-validation is used to evaluate each combination of parameters and the parameters that maximises accuracy or F1-score on test set across the folds is selected. The timeout is set to one hour for each benchmark. |