For a fraud detection model, would F1-score be a better choice than accuracy?

Fraud-detection-model-would-F1-score.md

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✅ Why F1-score Is Better:

The F1-score is the harmonic mean of precision and recall, and gives a more balanced picture:

$$ F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision + Recall}} $$

Where:

• Precision = TP / (TP + FP)
• Recall = TP / (TP + FN)

Example: https://www.kaggle.com/code/mayuringle8890/fraud-detection-notebook/

📌 In fraud detection:

• We want high recall → catch as many frauds as possible (reduce false negatives)
• We also want high precision → avoid too many false alarms (reduce false positives)

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⚖️ F1-Score Trade-off:

• A high F1-score means your model is doing well on both fronts.

• You may even consider using Fβ-score to prioritize either recall or precision depending on your business need:

  • F2-score if catching more fraud is more important than false positives.
  • F0.5-score if false positives are costlier than missing some fraud.

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✅ Why Accuracy Fails in Imbalanced Problems:

If 99.9% of transactions are legitimate and the model always predicts "Not Fraud", then:

• Accuracy = 99.9%,
• True Positives (fraud detected) = 0
• False Negatives (fraud missed) = All actual frauds

👉 So the model looks good on paper (99.9% accurate) but is completely useless in practice — it catches no fraud.

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🔍 In Summary:

Metric	Good for imbalanced data?	What it tells you
Accuracy	❌ No	Can be misleading if classes are imbalanced
Precision	✅ Yes	How many predicted frauds were actually frauds
Recall	✅ Yes	How many actual frauds you successfully detected
F1-score	✅✅ Best choice	Balances precision and recall

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For fraud detection, which is a highly imbalanced binary classification problem, you need a model that:

1. Handles class imbalance well.
2. Can capture complex patterns.
3. Can be tuned for precision-recall trade-offs.

Here are some recommended models, categorized by complexity:

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🔹 1. Baseline Models (Start Simple)

Model	Notes
Logistic Regression	Simple, interpretable, good baseline. Add class weights or use SMOTE.
Decision Tree	Captures non-linear patterns, but can overfit.

Use with:

• class_weight='balanced'
• feature scaling (for LR)

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🔹 2. Ensemble Models (Often Best for Tabular Fraud Data)

Model	Why it's good for fraud detection
Random Forest	Robust, handles imbalance with class weights.
XGBoost	Handles imbalance via scale_pos_weight, high performance.
LightGBM	Fast, efficient, supports is_unbalance=True flag.
CatBoost	Works well with categorical features and imbalance.

✅ These are often top performers in Kaggle competitions and real-world systems.

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🔹 3. Anomaly Detection Models (When labels are rare or noisy)

If you have very few fraud samples, try:

Model	Notes
Isolation Forest	Unsupervised, good for detecting rare patterns
One-Class SVM	Works when you only have "normal" data to learn from
Autoencoders (Deep Learning)	Learn normal patterns, flag large reconstruction errors as frauds

🔍 Use these when you don’t have labels for frauds or they are very sparse.

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🧪 Experimental / Advanced

Model	Notes
Graph Neural Networks	If fraud involves networks (users, devices, accounts)
Hybrid Models (Ensemble + Deep Learning)	Combine decision trees and autoencoders

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✅ Best Practice for Fraud Detection

• Resampling: SMOTE, ADASYN, or undersample the majority class.
• Evaluation: Use F1-score, Precision-Recall AUC, not accuracy.
• Threshold tuning: You can tune the classification threshold to optimize F1 or minimize business cost.
• Explainability: Use SHAP/LIME for model interpretability, especially important in finance.

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📌 Recommendation for You (If You’re Just Starting):

# Try this pipeline:
- Preprocessing: Scale features + encode categoricals
- Use: LightGBM or XGBoost
- Set: scale_pos_weight = (legit / fraud) ratio
- Evaluate: precision, recall, F1, PR-AUC

Example: https://www.kaggle.com/code/mayuringle8890/fraud-detection-notebook/

📌 Final Note:

In a real fraud detection system, you might also:

• Use Precision-Recall curves
• Optimize based on business cost (e.g., cost of a false positive vs false negative)
• Use confusion matrix to interpret model performance