Model Evaluation: Understanding Classification Metrics

Table of Contents

1. Challenge

   Introduction

   Introduction

   Welcome to the Model Evaluation Code Lab. In this hands-on lab, you'll learn to compute and interpret key classification metrics, build and analyze confusion matrices, and visualize model performance using ROC and Precision-Recall curves. By the end, you'll confidently evaluate machine learning models for real-world applications.

   Background

   You're a data scientist at CarvedRock evaluating medical diagnostic models. The team needs to select the best model for disease detection where false negatives are costly. A patient incorrectly classified as healthy (false negative) could miss critical treatment, while a false positive triggers additional testing.

   Your team has trained three classification models on patient data: Logistic Regression, Random Forest, and Gradient Boosting. Your job is to compute evaluation metrics, interpret confusion matrices, and create visualizations to recommend the optimal model for deployment.

   Familiarizing with the Program Structure

   The lab environment includes the following key files:

   1. data_loader.py: Loads and prepares the medical diagnostic dataset
   2. model_trainer.py: Contains pre-trained classification models
   3. metrics_calculator.py: Computes classification metrics (accuracy, precision, recall, F1)
   4. confusion_matrix_builder.py: Builds and displays confusion matrices
   5. visualization.py: Creates ROC and Precision-Recall curve plots
   6. evaluate_models.py: Main script to run full model evaluation
   7. compare_models.py: Compares all models and generates recommendations

   The environment uses Python 3.x with scikit-learn for machine learning, NumPy for numerical operations, Matplotlib and Seaborn for visualization. All dependencies are pre-installed in the lab environment.

   To run scripts, use the terminal with commands like python3 evaluate_models.py. The results will be saved to the output/ directory.

   Important Note: Complete tasks in order. Each task builds on the previous one. Test your code frequently by running the provided scripts to catch errors early.

2. Challenge

   Understanding Data and Basic Metrics

   Understanding Data Preparation

   Before evaluating any model, you need properly split data. The test set must remain unseen during training to give honest performance estimates. Using a fixed random seed ensures reproducibility - you'll get the same split every time, making debugging easier and results comparable.

   In machine learning, you typically split data into training (80%) and testing (20%) sets. The training set teaches the model patterns, while the test set measures how well those patterns generalize to new, unseen data. ### Understanding Classification Metrics

   A single accuracy number hides important details. Consider a disease affecting only 5% of patients - a model predicting "healthy" for everyone achieves 95% accuracy but catches zero sick patients!

   This is why you need multiple metrics:

   • Precision tells you how trustworthy positive predictions are (low false alarms)
   • Recall tells you how completely you catch positives (few missed cases)
   • F1 balances both metrics into a single score

   For medical diagnosis, missing a sick patient (low recall) is often worse than extra testing (low precision). Understanding these trade-offs is essential for choosing the right model.

3. Challenge

   Analyzing Prediction Errors

   Understanding the Confusion Matrix

   The confusion matrix is your diagnostic tool for understanding model errors. It's a 2x2 table for binary classification showing exactly which predictions are correct and which are mistakes:

                     Predicted
                     Neg    Pos
   Actual   Neg     [TN]   [FP]
            Pos     [FN]   [TP]
   

   • True Positives (TP): Correctly identified sick patients
   • True Negatives (TN): Correctly identified healthy patients
   • False Positives (FP): Healthy patients wrongly flagged as sick
   • False Negatives (FN): Sick patients missed (dangerous!)

   This granular view reveals patterns like "the model often predicts healthy when patients are actually sick," helping you decide if the model is safe for deployment.

4. Challenge

   Visualizing Model Performance

   Understanding ROC and Precision-Recall Curves

   Classification models output probabilities, not just 0/1 predictions. By varying the threshold (e.g., "predict sick if probability > 0.3"), you trade off between catching more sick patients (higher recall) and having more false alarms (lower precision).

   ROC Curve plots True Positive Rate (Recall) vs False Positive Rate:

   • A perfect classifier hugs the top-left corner
   • The diagonal line represents random guessing
   • AUC (Area Under Curve) summarizes performance: 1.0 = perfect, 0.5 = random

   Precision-Recall Curve is especially useful for imbalanced datasets:

   • Shows the trade-off between precision and recall at different thresholds
   • Better for situations where the positive class is rare (like disease detection)

   ROC and PR curves visualize these trade-offs across all possible thresholds, helping you choose the right operating point for your application.

5. Challenge

   Model Comparison and Evaluation

   Understanding Visualization for Decision Making

   Raw numbers like "AUC = 0.85" are hard to compare across models. Visualization makes differences obvious - you can see which model's curve is higher, where they cross, and at what threshold each performs best.

   For medical diagnosis:

   • Higher ROC curves mean better overall discrimination
   • PR curves that stay high as recall increases mean fewer missed diagnoses with acceptable false alarm rates

   This visual comparison is essential for presenting findings to stakeholders who need to choose a model for deployment. ### Understanding Model Selection

   The "best" model depends on your priorities. For medical diagnosis, you might choose the model with highest recall even if precision suffers - missing a sick patient is worse than extra testing. For spam filtering, precision might matter more - you don't want important emails in spam.

   This final task brings everything together: evaluate all models with the metrics you've implemented, visualize the comparisons, and make a justified recommendation based on the medical diagnosis context.

6. Challenge

   CONCLUSION

   CONCLUSION

   Congratulations on completing the Model Evaluation lab! You've successfully learned to compute, interpret, and visualize classification metrics for real-world medical diagnosis.

   What You've Accomplished

   Throughout this lab, you have:

   1. Configured Data Splitting: Set up reproducible train/test splits for honest model evaluation.
   2. Computed Classification Metrics: Calculated accuracy, precision, recall, and F1 score to evaluate model performance from multiple angles.
   3. Built Confusion Matrices: Constructed and interpreted confusion matrices to understand exactly where models make errors.
   4. Extracted Key Values: Isolated TP, TN, FP, FN to calculate derived metrics like specificity and false positive rate.
   5. Created ROC Curves: Plotted ROC curves and computed AUC scores to visualize model discrimination ability.
   6. Generated PR Curves: Built Precision-Recall curves for better evaluation on imbalanced datasets.
   7. Compared Multiple Models: Evaluated three classifiers and recommended the best one for medical diagnosis.

   Key Takeaways

   • Accuracy Isn't Everything: High accuracy can hide poor performance on minority classes.
   • Context Determines Priority: For medical diagnosis, recall (catching all sick patients) matters most.
   • Confusion Matrices Tell the Story: They show exactly where errors occur and their real-world impact.
   • Visualizations Aid Decisions: ROC and PR curves help compare models across all possible thresholds.
   • Imbalanced Data Needs Special Attention: PR curves are more informative than ROC when classes are imbalanced.