📢Day 10/100: Class Imbalance Challenges Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨
In my dataset:
Fraudulent transactions are rare (<5%), making prediction tricky. Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset. 💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.
💡 Question: What other methods do you use to address class imbalance without oversampling?
📢Day 10/100: Class Imbalance Challenges Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨
In my dataset:
Fraudulent transactions are rare (<5%), making prediction tricky. Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset. 💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.
💡 Question: What other methods do you use to address class imbalance without oversampling?
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from tw
