Baselines, training, and tuning
Start simple, then earn every bit of complexity It is tempting to open with a deep neural network. Resist. The professional move is to build the simplest thing that could possibly work first, then only add complexity that measurably pays off. Always build a baseline to beat A baseline is a deliberately simple reference: predict […]
Retraining without breaking things
The new model is better on average. That is not enough Retraining feels routine — fresh data in, better numbers out, ship it. The risk is that “better on average” hides “worse on the cases that matter”, and the people affected by those cases are precisely the ones who will notice. Compare on a fixed […]
Explaining a model to the people it affects
Three audiences, three explanations “Explainability” is treated as one requirement. It is three, and an explanation aimed at the wrong audience satisfies nobody. The person affected by the decision They need one thing: what would have to be different for a different outcome. “Your application was declined; with six months more trading history it would […]
Practical ML: From Idea to Model
Turn a fuzzy business goal into a concrete ML task, prepare honest data, beat a baseline, and ship a model you can monitor and trust.