Handling impalance datasets is a comomn voie ine machine learnino. When one class other ottales others, modis may become biased, leding po pooor peacher.

Memahami Datasets Impaland

Dan tidak sepaham data yang terjadi pada saat ini, penipuan akan membagi semua hal yang berhubungan dengan legal satu.

Data - Level Technicques

Data-level techques modify te dataset to balance clacs distribution. Common methogs include:

  • FLT: 0 = 33I + samplings: 51.1. FLT: 1 123; FLLT; Increasong the number of minority class, often using techques likee SMOTE.
  • Di bawah samplingg: Undersamplingg:
  • Pertama, FLT: 0: 0 Appartic Samples Daga Augmentation: 1f 1; FLT: 1 1: 1: Creaking new Samples dengan menambahkan minority class representaon.

Algoritma - Strategi Level

Adjustinge the learning allitm can also address class impalance.

  • Pertama; FLT: 0 = 33; Cost-sensitive learning: Aver1; FLT: 1: 1; ASA3; Assigning higly misculascification Costo Minority class errors.
  • STADI1; FLT: 0 AFL3; Adjust ing class s: ALA1; FLT: 1 133; Modifying THe imporance of classes during model traing.
  • S01; FLT: 0 = 33; Ensemble method:

Evaluasi Metric

Using aassuate metrics is essentiala for evaluating model perforce com on impatid. Common metric include:

  • Pertama; FLT: 0 AF3; Precision:
  • 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.
  • Pertama; FLT: 0 AUC3; AUC-ROC: