Statistical Machine Learning, Week 7, Part b: Evaluating Classification Models (ROC/PR curves)

#artificialintelligence #machinelearning #datascience
Evaluating classification models using decision boundaries in 2D provides a visual insight into how well a model separates different classes based on feature space. In 2D, the decision boundary represents the line or curve that distinguishes between two or more classes, and analyzing its shape and positioning can reveal model behavior, such as overfitting or underfitting. For instance, a simple linear boundary may indicate that the model captures only linear relationships, whereas more complex, curved boundaries suggest the model is learning non-linear patterns.
To further quantify performance, a confusion matrix is used, which summarizes the model's predictions in terms of true positives, true negatives, false positives, and false negatives. This matrix allows us to compute various metrics such as accuracy, precision, recall, and F1 score, providing a more detailed evaluation of how well the model performs across different classes, especially in the presence of class imbalance or when specific types of errors (e.g., false positives) are more critical. By combining decision boundaries with confusion matrix analysis, we gain both visual and quantitative insights into a model’s classification effectiveness.
The ROC Curve (Receiver Operating Characteristic Curve) plots the True Positive Rate (Recall) against the False Positive Rate (FPR), which is the proportion of actual negatives that are incorrectly classified as positives. The Area Under the ROC Curve (AUC-ROC) provides a single value to summarize performance; a higher AUC (closer to 1) indicates better classifier performance. ROC curves are useful for comparing classifiers across varying thresholds, but they may be less informative than Precision-Recall curves in imbalanced datasets, as ROC includes both true negatives and false positives in its calculation.