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.…
Regression algorithms are tools used to predict continuous numerical values, like as prices, temperatures, or sales numbers, by learning the mathematical relationship between features and targets. A model that appears to work is easy to build. Most of the effort in credible machine learning goes into establishing whether the appearance…
Regression algorithms are tools used to predict continuous numerical values, like as prices, temperatures, or sales numbers, by learning the mathematical relationship between features and targets. The purpose of a first ML model is not to prove that ML works. It is to determine whether the available data contain enough…
A number means nothing without something to compare it against. Previous blogs were about interrogating data before modelling it. This one is about interrogating a result before believing it, and the tool for that is always a comparison: against a deliberately stupid model, against measurement uncertainty, against what a chart…