Best Practices For Evaluating Predictive Analytics Models

I’m jealous of AI Because with a model you can measure confidence Imagine you could do that as a human? Measure how close or far off you are? here's how to measure for technical and non-technical teams For business teams: Run a ‘known answers’ test. Give the model questions or tasks where you already know the answer. Think of it like a QA test for logic. If it can't pass here, it's not ready to run wild in your stack. Ask for confidence directly. Prompt it: “How sure are you about that answer on a scale of 1-10?” Then: “Why might this be wrong?” You'll surface uncertainty the model won't reveal unless asked. Check consistency. Phrase the same request five different ways. Is it giving stable answers? If not, revisit the product strategy for the llm Force reasoning. Use prompts like “Show step-by-step how you got this result.” This lets you audit the logic, not just the output. Great for strategy, legal, and product decisions. For technical teams: Use the softmax output to get predicted probabilities. Example: Model says “fraud” with 92% probability. Use entropy to spot uncertainty. High entropy = low confidence. (Shannon entropy: −∑p log p) Language models Extract token-level log-likelihoods from the model if you have API or model access. These give you the probability of each word generated. Use sequence likelihood to rank alternate responses. Common in RAG and search-ranking setups. For uncertainty estimates, try: Monte Carlo Dropout: Run the same input multiple times with dropout on. Compare outputs. High variance = low confidence. Ensemble models: Aggregate predictions from several models to smooth confidence. Calibration testing: Use a reliability diagram to check if predicted probabilities match actual outcomes. Use Expected Calibration Error (ECE) as a metric. Good models should show that 80% confident = ~80% correct. How to improve confidence (and make it trustworthy) Label smoothing during training Prevents overconfident predictions and improves generalization. Temperature tuning (post-hoc) Adjusts the softmax sharpness to better align confidence and accuracy. Temperature < 1 → sharper, more confident Temperature > 1 → more cautious, less spiky predictions Fine-tuning on domain-specific data Shrinks uncertainty and reduces hedging in model output. Especially effective for LLMs that need to be assertive in narrow domains (legal, medicine, strategy). Use focal loss for noisy or imbalanced datasets. It down-weights easy examples and forces the model to pay attention to harder cases, which tightens confidence on the edge cases. Reinforcement learning from human feedback (RLHF) Aligns the model's reward with correct and confident reasoning. Bottom line: A confident model isn't just better - it's safer, cheaper, and easier to debug. If you’re building workflows or products that rely on AI, but you’re not measuring model confidence, you’re guessing. #AI #ML #LLM #MachineLearning #AIConfidence #RLHF #ModelCalibration

Thrilled to share our new The Lancet Digital Health Viewpoint on the chaotic universe of AI performance metrics colliding with the realities of clinical care. In this piece, we tackle a simple question: How should we actually evaluate predictive AI models intended for medical practice? With 32 different metrics circulating across discrimination, calibration, overall performance, classification, and clinical utility, it’s no wonder the field is confused and sometimes misled. Our analysis shows why selecting the right performance measures is not just a statistical preference but a clinical imperative. We highlight two essential characteristics that truly matter: 1. whether a metric is correct (optimized only when predicted probabilities are correct), and 2. whether it reflects statistical vs. decision-analytical performance in a way that aligns with real clinical consequences. The results are striking: some of the most widely used metrics, including the beloved F1 score, fail spectacularly when evaluated through a clinical lens. We offer clear recommendations: report AUC, calibration plots, net benefit with decision curve analysis, and probability-distribution plots. These metrics together provide the transparency and rigor required for safe, reliable deployment. Proud of this work, proud of the team Ben Van Calster Ewout Steyerberg Gary Collins Andrew Vickers Laure Wynants Maarten van Smeden Karandeep Singh and many others, and deeply hopeful that this brings more clarity, accountability, and clinical grounding to how we evaluate AI in healthcare.

Not all errors are equal. Some are worth fixing more than others. Imagine you’re building a model to predict customer churn. A false negative—predicting a customer will stay when they actually leave—can cost thousands of dollars in lost revenue. A false positive—predicting churn when the customer would have stayed—might only cost a small retention offer. Treating these mistakes as equal, like most accuracy metrics do, misses the bigger picture. This is where 𝐜𝐨𝐬𝐭-𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 comes in. Instead of optimizing for raw accuracy, you can tell your model which mistakes are more costly. In practice, this can be done by: 👉 Weighted loss functions: Modify your training loss to penalize false negatives more than false positives. For example, if using logistic regression or neural networks, you can apply class weights in cross-entropy loss. 👉 Resampling techniques: Oversample the minority “high-cost” class (in this case, churners) or undersample low-cost classes to bias the model towards minimizing high-impact mistakes. Even a well-trained model needs careful 𝐭𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝 𝐭𝐮𝐧𝐢𝐧𝐠. The default 0.5 probability isn’t always optimal. You can: 👉 Use business-driven thresholds: Choose the cutoff that maximizes expected revenue or minimizes cost based on your confusion matrix. 👉 Perform grid search or optimization over thresholds using your validation set and the monetary cost associated with each type of prediction. Another way to approach this is through 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐚𝐥𝐮𝐞 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠. Assign a real-world cost or gain to each type of prediction, compute the net expected gain over your validation set, and tune the model or threshold to maximize that. This moves the focus from “statistical correctness” to business impact. 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 also matters. High-confidence predictions are usually reliable, but when the model is unsure—like a probability near 0.5—you can: 👉 Flag these for human review. 👉 Use ensembles or Bayesian models to quantify uncertainty and guide intervention strategies. Finally, don’t forget 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐟𝐭𝐞𝐫 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭. The business environment changes, and so do the costs associated with errors. Regularly recalibrating your thresholds and retraining models ensures you continue focusing on the mistakes that matter most. The key takeaway: chasing perfect accuracy is rarely the goal. By understanding which errors are costly, adjusting your model to focus on them, and incorporating uncertainty into decisions, you build models that not only predict but actually deliver measurable business value.

*** How to Choose and Validate a Predictive Model *** Choosing a Predictive Model 1. **Define the Objective** - Clarify your prediction goal (e.g., classification vs. regression). - Identify the business or research objective behind the prediction. 2. **Understand Your Data** - Assess the size, quality, and data type (structured vs. unstructured). - Evaluate missing values and distributions, and identify potentially important features. 3. **Consider Model Complexity** - Simple models (e.g., linear regression, decision trees) are easier to interpret. - Complex models (e.g., random forests, neural networks) may provide higher accuracy but less transparency. 4. **Balance Bias and Variance** - Aim to avoid underfitting (high bias) and overfitting (high variance). - Utilize learning curves to diagnose model performance. 5. **Align with Resources** - Some models require more computational power or expertise for deployment and maintenance. Validating a Predictive Model 1. **Train/Test Split** - Divide the data into training and testing sets (e.g., 70% training and 30% testing) to estimate performance on unseen data. 2. **Cross-Validation** - Use k-fold cross-validation to reduce evaluation variance and improve model generalizability. 3. **Performance Metrics** - For classification: measure accuracy, precision, recall, F1-score, and AUC-ROC. - For regression: use RMSE, MAE, and R². 4. **Hyperparameter Tuning** - Employ grid search, random search, or Bayesian optimization to fine-tune model parameters. 5. **Model Interpretation** - Utilize tools like SHAP, LIME, or partial dependence plots to build trust and gain insights into the model’s decisions. --- B. Noted

I used to spend weeks trying to debug my ML apps. Until I discovered these 3 testing strategies 🧠 ↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 A better offline metric does NOT mean a better model, because → An offline metric (e.g test ROC) is *just* a proxy for the actual business metric you care about (e.g money lost in fraudulent transactions) → The ML model is just a small bit of the whole ML system in production So the question is: "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗯𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗼𝗳𝗳𝗹𝗶𝗻𝗲 𝗽𝗿𝗼𝘅𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗲𝘁𝗿𝗶𝗰𝘀?" 🤔 Here are 3 methods to evaluate your ML model, from less to more robust ↓ 1️⃣ 𝗕𝗮𝗰𝗸𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 1 → Pick a date D in the past. 2 → Use data to date D to train/test your model 3 → Use data from D onwards to estimate the impact of your model on the business metric (if that is possible). Pros and Cons ✅ No need to deploy the model. ❌ Tests only the ML model, and NOT the entire system ❌ It is often not possible to estimate the impact on business metrics unless the model is deployed. 2️⃣ 𝗦𝗵𝗮𝗱𝗼𝘄 𝗱𝗲𝗽𝗹𝗼𝘆 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 The model is deployed and used to predict but its output is NOT used by downstream services/human operators to take actions. Pros and Cons ✅ Tests that the entire ML system (not only the ML model) works as expected according to the proxy metric. ❌ Does not test the final impact on the business metric. 3️⃣ 𝗔/𝗕 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 You split your userbase into 2 groups: - Group A (control group) is not affected by your ML model. - Group B (test group) is affected by your ML model. The test runs for a few days, and at the end, you compare the business metric of Group A vs B. A/B testing is the most reliable way to test your ML model, before taking the last step and using the model for the entire user base. 𝗧𝗼 𝘀𝘂𝗺 𝘂𝗽 This is the path from offline proxies to real-world business metrics: 1 → Offline evaluation with proxy metric 2 → Backtest 3 → Shadow deployment 4 → A/B test 5 → 100% Deployment to production. ---- Hi there! It's Pau 👋 Every week I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #mlops #realworldml

Many teams overlook critical data issues and, in turn, waste precious time tweaking hyper-parameters and adjusting model architectures that don't address the root cause. Hidden problems within datasets are often the silent saboteurs, undermining model performance. To counter these inefficiencies, a systematic data-centric approach is needed. By systematically identifying quality issues, you can shift from guessing what's wrong with your data to taking informed, strategic actions. Creating a continuous feedback loop between your dataset and your model performance allows you to spend more time analyzing your data. This proactive approach helps detect and correct problems before they escalate into significant model failures. Here's a comprehensive four-step data quality feedback loop that you can adopt: Step One: Understand Your Model's Struggles Start by identifying where your model encounters challenges. Focus on hard samples in your dataset that consistently lead to errors. Step Two: Interpret Evaluation Results Analyze your evaluation results to discover patterns in errors and weaknesses in model performance. This step is vital for understanding where model improvement is most needed. Step Three: Identify Data Quality Issues Examine your data closely for quality issues such as labeling errors, class imbalances, and other biases influencing model performance. Step Four: Enhance Your Dataset Based on the insights gained from your exploration, begin cleaning, correcting, and enhancing your dataset. This improvement process is crucial for refining your model's accuracy and reliability. Further Learning: Dive Deeper into Data-Centric AI For those eager to delve deeper into this systematic approach, my Coursera course offers an opportunity to get hands-on with data-centric visual AI. You can audit the course for free and learn my process for building and curating better datasets. There's a link in the comments below—check it out and start transforming your data evaluation and improvement processes today. By adopting these steps and focusing on data quality, you can unlock your models' full potential and ensure they perform at their best. Remember, your model's power rests not just in its architecture but also in the quality of the data it learns from. #data #deeplearning #computervision #artificialintelligence

✋Before rushing into training models, do not skip the part that actually determines whether the model is useful: Measuring performance. Without the right metrics you are not evaluating a model, you are just validating your assumptions. Check out theses nine metrics every ML practitioner should understand and use with intention 👇 1. Accuracy Good for balanced datasets. Misleading when classes are skewed. 2. Precision Of the samples you predicted as positive, how many were correct. Important when false positives are costly. 3. Recall Of the samples that were actually positive, how many you caught. Critical when false negatives are dangerous. 4. F1 Score Balances precision and recall. Reliable when you need a single metric that reflects both types of error. 5. ROC AUC Measures how well a model separates classes across thresholds. Useful for model comparison independent of cutoffs. 6. Confusion Matrix Exposes the exact distribution of true positives, false positives, true negatives, and false negatives. Great for diagnosing failure modes. 7. Log Loss Penalizes confident wrong predictions. Important for probabilistic models where calibration matters. 8. MAE (Mean Absolute Error) Average of absolute errors. Simple, interpretable, and robust for many regression problems. 9. RMSE (Root Mean Squared Error) Heavily penalizes large errors. Best when you care about avoiding big misses. Strong ML systems are built by measuring the right things. These metrics show you how your model behaves, where it fails, and whether it is ready for production. What else would you add? #AI #ML