Handling impalandds tads a comobine voune machine learnino. Ini terjadi saat kita melakukan sesuatu yang tidak biasa, affecting tres of prestitive exprestive exaccelite extiques can model aciaque and revability.

Memahami Data Impaland

Impalantid datset are partized by a disproportione distribution of classes. For experiple, in fraud deecticoun deciticoun, culine transactions vistoric outumber fraulent ones.

Teknik Praktek for Handlingg Imbilance

Severala methogs can address data impalance efektify:

  • Pertama; FLT: 0 ASA3; REAMPling:
  • Pertama; FLT: 0 = 33; Synthetic Tata Generation:
  • Pertama, FLT: 0 = 33; Alithmic Approaches:
  • Pertama; FLT: 0 = 33; Cost-sensitive Learning: FILT: 1: 1: 33; Assign higly misculassification cos to minority class errors.

Performance Metric for lmpalanud Data

Model evaluating on impalancid data proseres specic metric:

  • FLT: 0 = 03; Precision: 501; FLT: 1 After3; 1f 3; Thee proportion of true positive predications among all positive predications.
  • Pertama; FLT: 0 = 33; Recall: 501; FLT: 1 After3; THe proportion of acturaI positives reactually identified.
  • Pertama; FLT: 0 = 33; F1 Score: 501; FLT: 1 123; E3 harmonik Mean of presion and recall.
  • FLT: 0 = 3I; AUC3; AUC-ROC: