Why Accuracy Alone Can Be Misleading

Accuracy tells us how often our model is right, but what it doesn’t reveal can be crucial to understanding its true impact

Accuracy, defined as the ratio of correct predictions to total predictions, is an easy-to-calculate and intuitive metric. However, it falls short in cases where class imbalances exist, such as in fraud detection or rare disease identification, where a model might score highly in accuracy while failing at its intended purpose. For example, in a dataset where 95% of instances are non-fraudulent, a model that always predicts “non-fraud” would achieve 95% accuracy yet fail at identifying any fraudulent transactions.

Precision and Recall: Measuring Specificity and Sensitivity

When false positives or false negatives come at a high cost, precision and recall offer more insight than accuracy

Precision is the proportion of positive predictions that are actually correct, giving us an idea of how many false positives the model generates. In contrast, Recall measures the proportion of actual positives that are correctly identified by the model, providing insight into false negatives. These metrics are particularly valuable in domains like healthcare, where missing a positive case (false negative) can have serious consequences.

Formula Recap:

• Precision = True Positives / (True Positives + False Positives)
• Recall = True Positives / (True Positives + False Negatives)

Use Case: Fraud Detection In fraud detection, a high recall is often more desirable because missing fraudulent transactions (false negatives) is costlier than flagging legitimate ones as fraud (false positives). However, models that optimize only for recall risk inundating analysts with false positives, which is where F1 Score comes into play.

F1 Score: Balancing Precision and Recall

When you need to strike a balance between precision and recall, the F1 Score is the go-to metric

The F1 Score is the harmonic mean of precision and recall, offering a balanced view that accounts for both false positives and false negatives. This metric is especially useful in cases where there is no preference between precision and recall but both are critical.

Formula Recap:

• F1 Score = 2 × (Precision × Recall) / (Precision + Recall)

Use Case: Customer Churn Prediction For a churn model, both precision and recall are important. High precision ensures that the efforts to retain customers are focused on those most likely to churn, while high recall ensures that the model captures as many potential churn cases as possible.

AUC-ROC: Understanding True Positive and False Positive Rates

AUC-ROC measures your model’s ability to distinguish between classes across all possible threshold values

The Receiver Operating Characteristic (ROC) Curve plots the true positive rate against the false positive rate at various threshold settings, while the Area Under the Curve (AUC) score summarizes the model's ability to discriminate between positive and negative cases. A model with an AUC close to 1 indicates good separability, while an AUC of 0.5 suggests random guessing.

Use Case: Marketing Campaign Targeting In campaign targeting, AUC-ROC can help ensure that the model accurately separates likely responders from non-responders. This way, resources are optimized by focusing only on potential responders, reducing unnecessary spend on low-potential customers.

Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE): Evaluating Regression Models

In regression tasks, error metrics like MAE and RMSE provide insight into the magnitude of prediction errors

For regression models, accuracy metrics are not applicable. Instead, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are commonly used to assess performance:

• MAE provides an average magnitude of errors, treating all errors equally.
• RMSE penalizes larger errors more heavily, making it useful in cases where large errors are particularly undesirable.

Use Case: Sales Forecasting When forecasting sales, RMSE may be preferable to MAE as it penalizes large deviations, ensuring the model is more reliable in high-stakes situations where large errors are costly.

Cost-Sensitive Metrics: When Misclassifications Come with Different Costs

When certain misclassifications have higher consequences, cost-sensitive metrics help you account for these distinctions

Some applications, like credit risk scoring or medical diagnosis, have asymmetric costs for different types of errors. Cost-sensitive metrics incorporate these costs into the evaluation, allowing for optimization that considers the financial or operational impact of misclassifications.

Use Case: Credit Risk Scoring In credit scoring, false positives (predicting good credit when it's actually risky) can be more costly than false negatives. Cost-sensitive metrics help balance these risks by adjusting the model’s behaviour based on the specific business impact of each error type.

Calibration Curves: Ensuring Probabilistic Predictions Are Reliable

In probabilistic models, a well-calibrated model provides probability scores that reflect true likelihoods

Calibration curves plot predicted probabilities against observed outcomes, revealing how closely the model’s confidence aligns with actual results. A perfectly calibrated model ensures that predictions made with, for example, 70% confidence indeed occur 70% of the time. This is especially important in risk management and decision support applications.

Use Case: Insurance Premium Setting For an insurance model that predicts the likelihood of a claim, accurate calibration is essential for setting appropriate premiums. A poorly calibrated model could either overestimate or underestimate risks, leading to financial losses.

SHAP Values: Interpretability Metrics for Feature Importance

Beyond predictive performance, SHAP values provide transparency into the drivers behind a model’s predictions

Interpretability is often crucial in regulated industries or customer-facing applications where understanding the model’s decision process builds trust. SHAP (SHapley Additive exPlanations) values offer a way to quantify the impact of each feature on a prediction, providing insights into the “why” behind the model’s outcomes.

Use Case: Customer Loan Approvals In lending, SHAP values enable institutions to explain why a loan application was approved or denied, fostering transparency and compliance with regulatory requirements.

Choosing the Right Metrics: A Context-Driven Approach

Ultimately, choosing evaluation metrics depends on the problem context and the specific goals of the project. Here’s a quick guideline:

1. Imbalanced Classification: Use precision, recall, F1 score, and AUC-ROC.
2. Regression: Opt for MAE or RMSE, depending on error sensitivity.
3. Cost-Sensitive Contexts: Consider cost-sensitive metrics.
4. Probabilistic Predictions: Use calibration curves.
5. Interpretability: Explore SHAP values for feature impact.

Conclusion: A Holistic Approach to Model Evaluation

As ML applications grow more sophisticated, so too must our approach to evaluating models. Moving beyond accuracy allows us to create models that not only perform well but also align with the specific needs and constraints of their applications. By embracing a variety of evaluation metrics, we can develop ML models that drive meaningful impact, foster trust, and support better decision-making.

Key Takeaway: In machine learning, accuracy is just the beginning. The true measure of a model's success lies in its ability to meet the unique demands of its application.