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https://github.com/Doctorado-ML/benchmark.git
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2.0 KiB
2.0 KiB
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* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False Discretized: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 *
* Score is accuracy *
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Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
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* ✔ Equal to best.................: 1 *
* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
*************************************************************************************************************************
* STree ver. 1.2.3 Python ver. 3.11x with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07 *
* With gridsearched hyperparameters *
* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False Discretized: False *
* Execution took 624.25 seconds, 0.17 hours, on iMac27 *
* Score is accuracy *
*************************************************************************************************************************
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
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* ✔ Equal to best.................: 1 *
* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
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