Understanding Model Performance and Business Metrics
When evaluating a machine learning (ML) model, it’s important to look at two types of metrics:
- Model performance metrics – How well the model works from a technical point of view.
- Business metrics – How well the model adds value to the business.
Both are necessary to measure the real success of an ML solution.
📊 Model Performance Metrics
These metrics measure the technical quality of your model — how accurately it makes predictions.
✅ Accuracy
What it is:
The percentage of correct predictions made by the model.
Formula:
(Correct Predictions) / (Total Predictions)
When to use:
Best when classes are balanced (e.g., spam vs. not spam).
🧪 Precision
What it is:
The proportion of predicted positives that are actually correct.
Formula:
True Positives / (True Positives + False Positives)
Use case:
Important when false positives are costly (e.g., spam filters).
🔍 Recall (Sensitivity)
What it is:
The proportion of actual positives that the model correctly identified.
Formula:
True Positives / (True Positives + False Negatives)
Use case:
Important when false negatives are costly (e.g., disease detection).
⚖️ F1 Score
What it is:
The harmonic mean of precision and recall. It balances both metrics.
Formula:
2 * (Precision * Recall) / (Precision + Recall)
Use case:
Best when you need a balance between precision and recall.
📈 AUC (Area Under the ROC Curve)
What it is:
Measures how well the model can distinguish between classes.
Scale:
0.5 = random guessing, 1.0 = perfect model
Use case:
Great for evaluating binary classifiers, especially with imbalanced datasets.
📉 Mean Squared Error (MSE) / Root Mean Squared Error (RMSE)
What it is:
Used in regression tasks to measure the average error between predicted and actual values.
Use case:
Predicting continuous values (e.g., price, temperature).
💼 Business Metrics
These metrics evaluate whether the model delivers business value and aligns with company goals.
💵 Cost Per User
What it is:
How much it costs to serve each user using the ML solution.
Use case:
Used to manage infrastructure or API costs in scalable apps.
📦 Development Cost
What it is:
Time, labor, and infrastructure cost required to build, test, and deploy the ML model.
Use case:
Helps evaluate whether the model is worth building in-house vs. using a pre-trained model or third-party service.
📣 Customer Feedback
What it is:
User satisfaction, complaints, and suggestions after deploying the ML feature.
Use case:
Measures how well the model improves user experience (e.g., chatbot helpfulness, recommendation relevance).
📈 Return on Investment (ROI)
What it is:
Measures the financial gain from using the model compared to the cost of developing and maintaining it.
Formula:
(Total Benefits - Total Costs) / Total Costs
Use case:
Used by stakeholders to justify continued investment in ML.
📋 Summary Table
| Metric Type | Name | What It Measures | When to Use |
|---|---|---|---|
| Model | Accuracy | Overall correctness | Balanced datasets |
| Model | Precision | Correctness of positive predictions | When false positives are costly |
| Model | Recall | Coverage of actual positives | When false negatives are costly |
| Model | F1 Score | Balance of precision and recall | General classification performance |
| Model | AUC | Class separation ability | Binary classification problems |
| Model | MSE / RMSE | Prediction error in regression | Forecasting, numeric predictions |
| Business | Cost Per User | Cost to serve each user | Scalable AI services |
| Business | Development Cost | Time and money spent building the model | Budget planning and evaluation |
| Business | Customer Feedback | User satisfaction and value | Post-deployment quality assessment |
| Business | ROI | Financial return vs. model cost | Stakeholder decision-making |
✅ Final Note
A high-performing model is not enough unless it also brings value to the business. Combining technical and business metrics ensures your ML solution is successful from all angles.