From business question to ML task
Turn a business wish into a prediction the model can make “Reduce churn.” “Grow revenue.” “Cut support costs.” These are business goals, not machine-learning tasks. A model cannot predict a wish — it can only predict a specific, measurable thing about a specific unit. Your first and most important job is translation. Name the unit […]
Bias is in the data before it is in the model
A model trained on past decisions reproduces past decisions Machine learning finds patterns in history. Where that history contains human decisions, it contains their biases too — and the model will learn those as faithfully as anything else, then apply them at a scale and speed no human could. Removing the field does not remove […]
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.