Define prediction time before selecting features
A field is useful only if it exists when the decision is made.
Read the guideMachine learning · Practical evaluation
A model score needs a believable test.
Working notes for teams building modest, inspectable machine-learning experiments. Follow the data, establish a baseline and keep the final test honest.
Start with the field guideA working sequence · Start with the question
Notes for the work ahead
Build the baseline before tuning the model
A comparison is useful when both sides get the same test.
Read the guideKeep preprocessing inside the training boundary
The split applies to transformations as well as the estimator.
Read the guideSelect a heuristic without spending the final test
A runnable synthetic example with separate fitting, selection and final evaluation.
Read the guideA result card another person can rerun
Keep the task, split, baseline, error counts and reproduction details with the score.
Read the guideMake it practical
Experiment release check
Use this before sharing a model result. Checked items do not guarantee validity; retain the underlying evidence.
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