Key Lessons 1. Reproducibility is more than saving a trained model. A reproducible ML experiment preserves the data definition, preprocessing, feature set, target, validation strategy, random seeds, hyperparameters, software environment, evaluation results, and code. 2. Model reproducibility and experiment reproducibility are different. Reloading model.joblib and getting identical predictions proves…
Objective Determine whether model-guided experimental design reduces the number of trials required to reach a performance target, using a dataset with enough signal and samples to verify the pipeline behaves correctly. Dataset UCI Concrete Compressive Strength. 1,030 mix designs with measured compressive strength. Inputs: cement, slag, fly ash,…
1. The governing idea A model that knows what it doesn't know can tell you what to do next. Every model built before produced point predictions, useful for understanding a process, useless for deciding which experiment to run. A prediction of 58 MPa gives no basis for choosing…
Before asking what a model learned, establish whether there was anything to learn. Interpretability tools explain any model given to them, including models that perform worse than predicting the mean. They have no concept of whether the model is any good. A convincing SHAP plot of a worthless model is…
Key lessons 1. Explainability asks why a model made a prediction. It does not automatically explain why the physical material behaves the way it does. 2. Global and local explanations answer different questions. Global explanations describe which features generally influence model predictions; local explanations describe why one particular prediction was…
Analysis of whether a machine learning classifier should replace or supplement end-of-line inspection Recommendation: Do not deploy this model as an automated inspection gate. At current cost assumptions, inspecting 100% of output is approximately seven times cheaper than any model-based policy that respects operational constraints. Model-guided…
Contents 1. The governing idea 2. Key terminology, defined against the inspection problem 3. Principles 4. Case study: SECOM wafer screening 5. Quick reference 1. The governing idea A classifier does not make a decision. It produces a probability, and someone has to choose where to draw the line. That…
1. Classification predicts categories rather than continuous quantities. Regression asks “what strength?”, while classification can ask “PASS or FAIL?” 2. The class definition is an engineering decision. Our 110 kPa threshold created the PASS/FAIL labels. In a real project, that threshold needs a justified specification or decision basis. 3.…