Threshold Moving & Focal Loss: Smarter Strategies for Imbalanced Classification

In machine learning, class imbalance is a common challenge, especially in domains like fraud detection, medical diagnosis, and rare event prediction. When one class significantly outnumbers another, models often become biased toward the majority class, leading to poor performance on the minority class. Two effective techniques for addressing this issue are Threshold Moving and Focal Loss. These methods provide more control over classification decisions and improve predictive performance.

Let’s explore how these techniques compare to other loss functions, their advantages, and their role in modern classification strategies.

Threshold Moving: A Simple Yet Powerful Technique

What is Threshold Moving?

Threshold moving involves adjusting the decision threshold that converts predicted probabilities into class labels. Most models use a default threshold of 0.5, meaning predictions above 0.5 are classified as the positive class. However, in imbalanced datasets, this can lead to underprediction of the minority class.

How It Works

1. Train a model using standard classification techniques.
2. Predict probabilities on the test dataset.
3. Experiment with different thresholds to find the one that optimizes a chosen metric (F1-score, G-Mean, Precision-Recall AUC).
4. Use the selected threshold for future predictions.

Key Advantages of Threshold Moving

? Better Minority Class Recognition: Shifting the threshold makes it easier for the model to classify minority instances correctly.

? Adaptability to Business Needs: Depending on the problem, we can balance false positives vs. false negatives (e.g., in fraud detection, it’s better to have some false alarms than missing real fraud).

? Computational Efficiency: Unlike complex resampling methods, threshold tuning requires no additional data processing.

Focal Loss: Prioritizing Hard-to-Classify Cases

What is Focal Loss?

Focal Loss is a modified version of cross-entropy loss designed to focus more on hard-to-classify examples while reducing the influence of easily classified ones.

It introduces a scaling factor that down-weights well-classified samples, allowing the model to focus on challenging cases where misclassification is more likely.

How It Works

The standard cross-entropy loss is modified by adding a tunable parameter γ (gamma), which controls how much emphasis is placed on misclassified instances:

• If a sample is misclassified (low probability for the correct class), the loss remains high.
• If a sample is easily classified (high probability for the correct class), its contribution to the loss diminishes.

Focal Loss vs. Other Loss Functions for Imbalance

Why Choose Focal Loss?

? Reduces Model Bias: Prevents the model from focusing too much on the majority class.

? Smooth Learning Curve: Helps avoid overwhelming the model with easy examples.

? Works Well in Semi-Supervised Learning: Particularly useful when using pseudo labels in weakly labeled datasets.

FocalMatch: Enhancing Focal Loss for Unlabeled Data

FocalMatch is an extension of focal loss designed for semi-supervised learning. It dynamically adjusts loss weights for unlabeled data, ensuring that pseudo-labeled examples are weighted appropriately based on their confidence.

How FocalMatch Works

1. Pseudo-labels are generated for unlabeled data.
2. Confidence scores determine whether the pseudo-label should contribute significantly to the loss.
3. Focal Loss scaling is applied to ensure uncertain pseudo-labels do not dominate training.

By fine-tuning the balance between real and pseudo-labeled data, FocalMatch improves performance when labeled data is scarce.

The Role of Decision Thresholds in Model Performance

The choice of decision threshold has a direct impact on classification outcomes:

• Lowering the threshold (e.g., 0.3 instead of 0.5) increases recall, capturing more minority class instances but at the cost of more false positives.
• Raising the threshold (e.g., 0.7) increases precision, reducing false positives but missing more minority class instances.

Threshold Selection: ROC vs. Precision-Recall Curves

When choosing the optimal threshold, two key evaluation metrics come into play:

• ROC AUC measures the trade-off between true positive rate and false positive rate. However, when one class is rare, the false positive rate may not provide enough insight.
• Precision-Recall AUC is better for imbalanced datasets as it focuses directly on precision and recall trade-offs.

Best Practice: For heavily imbalanced problems, optimize thresholds based on Precision-Recall AUC rather than ROC AUC.

Final Thoughts: Combining Strategies for Maximum Impact

? Use Threshold Moving to fine-tune classification outputs and improve recall without altering the training process.

? Use Focal Loss to enhance model learning by prioritizing hard-to-classify examples.

? Consider FocalMatch for semi-supervised learning where labeled data is limited.

? Select thresholds based on Precision-Recall AUC for imbalanced datasets.

By integrating these techniques, machine learning practitioners can significantly improve classification performance, ensuring that minority class predictions are not overlooked.

Let’s discuss! Have you used threshold tuning or focal loss in your models? What were your results? Share your thoughts in the comments!