Data-Driven Assessment Models

One of the hardest challenges for product teams is deciding which features make the roadmap. Here are ten methods that anchor prioritization in user data. MaxDiff asks people to pick the most and least important items from small sets. This forces trade-offs and delivers ratio-scaled utilities and ranked lists. It works well for 10–30 features, is mobile-friendly, and produces strong results with 150–400 respondents. Discrete Choice Experiments (CBC) simulate realistic trade-offs by asking users to choose between product profiles defined by attributes like price or design. This allows estimation of part-worth utilities and willingness-to-pay. It’s ideal for pricing and product tiers, but needs larger samples (300+) and heavier design. Adaptive CBC (ACBC) builds on this by letting users create their ideal product, screen unacceptable options, and then answer tailored choice tasks. It’s engaging and captures “must-haves,” but takes longer and is best for high-stakes design with more attributes. The Kano Model classifies features as must-haves, performance, delighters, indifferent, or even negative. It shows what users expect versus what delights them. With samples as small as 50–150, it’s especially useful in early discovery and expectation mapping. Pairwise Comparison uses repeated head-to-head choices, modeled with Bradley-Terry or Thurstone scaling, to create interval-scaled rankings. It works well for small sets or expert panels but becomes impractical when lists grow beyond 10 items. Key Drivers Analysis links feature ratings to outcomes like satisfaction, retention, or NPS. It reveals hidden drivers of behavior that users may not articulate. It’s great for diagnostics but needs larger samples (300+) and careful modeling since correlation is not causation. Opportunity Scoring, or Importance–Performance Analysis, plots features on a 2×2 grid of importance versus satisfaction. The quadrant where importance is high and satisfaction is low reveals immediate priorities. It’s fast, cheap, and persuasive for stakeholders, though scale bias can creep in. TURF (Total Unduplicated Reach & Frequency) identifies combinations of features that maximize unique reach. Instead of ranking items, it tells you which bundle appeals to the widest audience - perfect for launch packs, bundles, or product line design. Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) are structured decision-making frameworks where experts compare options against weighted criteria. They generate transparent, defensible scores and work well for strategic decisions like choosing a game engine, but they’re too heavy for day-to-day feature lists. Q-Sort takes a qualitative approach, asking participants to sort items into a forced distribution grid (most to least agree). The analysis reveals clusters of viewpoints, making it valuable for uncovering archetypes or subjective perspectives. It’s labor-intensive but powerful for exploratory work.

42.1% error reduction with 85% less data. At Ento, we use a lot of traditional black-box Machine Learning to model building energy consumption, and they're great for many use cases. But they have their limits. When we're dealing with: - Plenty of indoor sensor data - Limited historical data - The need to actively control a building’s HVAC system ... plain black-box approaches often fall short. That’s why I’ve been following key trends around blending data-driven methods with physical modeling: 🔹 Transfer Learning: Use data from similar buildings to improve models. 🔹 Digital Twins: Blend data-driven methods and physical simulations. 🔹 Physics-Informed AI: Embed physical laws into the learning process to improve results. Just last month, three papers in these fields came out from leading researchers: - GenTL: A universal model, pretrained on 450 building archetypes, achieved a 42.1% average error reduction when fine-tuned with 85% less data. From Fabian Raisch et al. - An Open Digital Twin Platform: Han Li and Tianzhen Hong from LBNL built a modular platform that fuses live sensor data, weather feeds, and physics-based EnergyPlus models. - Physics-informed modeling: A new study proved that Kolmogorov–Arnold Networks (KANs) can rediscover fundamental heat transfer equations. From Xia Chen et al. Which of these 3 trends do you see having the biggest real-world impact in the next 2-3 years?

Evaluation is the key to successful model development! Reward Models and LLM as a Judge are often used as replacements for human evaluation but require costly preference data! Meta tries to solve this using an iterative self-improvement method and synthetic data generation to improve LLM Evaluators without human annotations. With this method, they improved Llama3-70B Instruct on RewardBench by 13%. 👀 Implementation 1️⃣ Collect a dataset of instructions covering various topics and complexities. 2️⃣ Prompt LLM to generate two responses, 1x high-quality response and 1x intentionally sub-optimal response (e.g., by introducing errors or omitting critical information). 3️⃣ Use the model as an LLM to generate reasoning traces and judgments for these pairs. 4️⃣ Train the LLM on the synthetic preference data, including reasoning and final judgments. 5️⃣ Use the improved LLM evaluator to generate better judgments on the synthetic data. 6️⃣ Retrain the LLM evaluator with these self-improved judgments. Repeat Steps 2-6 using the previous Evaluator for generation, judgments, and then training. 🔄 Insights 📈 Improved Llama 3 70B on RewardBench from 75.4% to 88.3%. 🤖 Achieved comparable results as models trained on human-labeled data. 🔧 Synthetic approach allows generating Evaluators based on custom criteria, e.g. always include citations. 🔄 Iterative approach leads to incremental performance gains. 🚨 Initial LLM biases might be amplified during the iterative approach. Paper: https://lnkd.in/eaMBHPmy Github: https://lnkd.in/eb7zNJsd Models: https://lnkd.in/e-XKD83X Dataset: https://lnkd.in/et5R4qWV

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