Regression with reject option and application to kNN
Authors: Ahmed Zaoui, Christophe Denis, Mohamed Hebiri
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, a numerical study is performed to illustrate the benefit of using the proposed procedure. |
| Researcher Affiliation | Academia | Ahmed Zaoui LAMA, Université Gustave Eiffel ahmed.zaoui@univ-eiffel.fr Christophe Denis LAMA, Université Gustave Eiffel MIA-Paris, Agro Paris Tech, INRAE, Université Paris-Saclay christophe.denis@univ-eiffel.fr Mohamed Hebiri LAMA, Université Gustave Eiffel CREST, ENSAE, Institut Polytechnique de Paris mohamed.hebiri@univ-eiffel.fr |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' sections, nor any structured code-like blocks. |
| Open Source Code | Yes | The code used for the implementation of the plug-in ε-predictor can be found at https://github.com/Zaoui Amed/Neurips2020_ Reject Option. |
| Open Datasets | Yes | The performance is evaluated on two benchmark datasets: QSAR aquatic toxicity and Airfoil Self Noise coming from the UCI database. |
| Dataset Splits | Yes | For all datasets, we split the data into three parts (50 % train labeled, 20 % train unlabeled, 30 % test). |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | For random forests and svm procedures, we used respectively the R packages random Forest and e1071 with default parameters. |
| Experiment Setup | Yes | We employ the 10-fold cross-validation to select the parameter k {5, 10, 15, 20, 30, 50, 70, 100, 150} of the k NN algorithm. |