Finite Carbon

Imagine you’re working on a machine learning model to identify areas affected by deforestation. The dataset contains satellite images, and your task is to classify image patches into two categories: “Deforested” (Class A) and “Non-Deforested” (Class B). However, deforested patches are significantly rarer than non-deforested ones.

Data Description:

• Class A (Deforested; at most 5% of the dataset): Represents the rare instances of deforested patches (e.g., cleared land, logging areas).
• Class B (Non-Deforested): Dominates the dataset and includes natural forest cover, agricultural land, and other non-deforested regions.

Classifier Performance Metrics:

• Accuracy: While overall accuracy is commonly used, it can be misleading due to class imbalance. In this case, achieving high accuracy might not reflect the model’s true performance.
• Precision: Precision measures the proportion of correctly predicted deforested instances (Class A) out of all predicted positive instances. Minimizing false positives is crucial to avoid misclassifying non-deforested areas.
• Recall (Sensitivity): Recall calculates the proportion of true deforested instances (Class A) correctly identified out of all actual deforested instances. High recall ensures we don’t miss deforested areas.
• Specificity: Specificity represents the proportion of true non-deforested instances (Class B) correctly identified out of all actual non-deforested instances. Avoiding false alarms for natural forest cover is essential.
• F1 Score: The F1 Score balances precision and recall, considering both false positives and false negatives. It’s particularly useful for imbalanced data.

Challenge:

• How would you design a binary classifier that optimizes both precision (minimizing false positives) and recall (minimizing false negatives) for detecting deforested areas?
• Consider techniques like oversampling deforested patches, using weighted loss functions, or leveraging synthetic data generation.
• Which evaluation metric(s) would you prioritize when evaluating your model’s performance on deforestation detection?

Remember, in rare-case binary classification, thoughtful model selection, feature engineering, and robust evaluation are critical to achieving reliable results, especially when dealing with imbalanced data.