MATLAB Program for Plotting ROC Curves with Implementation Details

ROC curves serve as essential tools for evaluating classification model performance, visualizing the relationship between True Positive Rate (TPR) and False Positive Rate (FPR) to demonstrate classifier behavior across different thresholds. In MATLAB, generating ROC curves typically involves the following implementation steps:

Model Predictions and True Labels Preparation The first step requires obtaining model prediction probabilities/scores and corresponding true labels (0 or 1). Prediction probabilities are typically derived from classification model outputs (such as logistic regression, SVM, etc.), while true labels represent known ground truth classifications. Key MATLAB functions for this step include loading data files and organizing prediction-output pairs.

TPR and FPR Calculation By adjusting classification thresholds, compute True Positive Rate (TPR) and False Positive Rate (FPR) at each threshold. TPR represents the proportion of correctly classified positive samples, while FPR indicates the proportion of incorrectly classified negative samples. The implementation involves creating threshold arrays and calculating confusion matrix metrics using vectorized operations.

Sorting and Threshold Iteration Sort prediction probabilities in descending order and iterate through all possible thresholds (typically using prediction probabilities as candidate thresholds). For each threshold, compute corresponding TPR and FPR values. This can be implemented efficiently using MATLAB's sort function and array operations to avoid explicit loops.

ROC Curve Plotting Use MATLAB's `plot` function to create the ROC curve with FPR on the X-axis and TPR on the Y-axis. Classifier performance improves as the curve approaches the top-left corner. Additional plotting enhancements include adding diagonal reference lines, axis labels, and legends using `xlabel`, `ylabel`, and `legend` functions.

AUC Calculation (Area Under Curve) AUC quantifies the classifier's overall performance as the area under the ROC curve. Values closer to 1 indicate superior model performance. Implementation methods include using the trapezoidal rule with `trapz` function or MATLAB's built-in `perfcurve` function for integrated AUC computation.

MATLAB provides built-in functions like `perfcurve` to simplify ROC curve computation and plotting, but manual implementation offers deeper understanding of the underlying principles. Program annotations typically detail the computational logic at each step, helping users comprehend how ROC curves are generated from prediction results. The manual approach demonstrates threshold selection algorithms and performance metric calculations that underlie the automated functions.