يعرض 2,461 - 2,480 نتائج من 8,895 نتيجة بحث عن 'significant ((((((gap decrease) OR (we decrease))) OR (teer decrease))) OR (mean decrease))', وقت الاستعلام: 0.72s تنقيح النتائج
  1. 2461

    Sound evoked cortical activity across vigilance states before and after NOE. حسب Linus Milinski (10532121)

    منشور في 2024
    "…Panels left to right: Ferret 1, Ferret 2, Ferret 3). Due to decreased signal quality in the six months post NOE assessment in Ferrets 2 and 3, AERs could not be quantitatively assessed for these timepoints. …"
  2. 2462

    Fig 9 - حسب Torsten Schober (20485754)

    منشور في 2024
  3. 2463

    Fig 8 - حسب Torsten Schober (20485754)

    منشور في 2024
  4. 2464

    Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML حسب Ayush Garg (21090944)

    منشور في 2025
    "…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …"
  5. 2465
  6. 2466
  7. 2467
  8. 2468
  9. 2469
  10. 2470
  11. 2471
  12. 2472

    Fig 4 - حسب Leena Inkilä (20614460)

    منشور في 2025
  13. 2473
  14. 2474
  15. 2475
  16. 2476
  17. 2477
  18. 2478
  19. 2479
  20. 2480