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Two ideas that have been sitting with me: why your emotional reactions are usually just a story you made up, and why waiting for a "big bang" breakthrough is a trap. Your outcomes are governed by quiet, invisible micro-shifts, not sudden moments of passion.

Picking up from hash-based splitting, this post tackles the another hurdle: ensuring your train/test buckets actually represent your target population. Learn how to strategically use stratified sampling, why domain knowledge is irreplaceable, and how to safely combine stratification with SMOTE without poisoning your evaluation.

Why relying on random_state for reproducible train/test splits breaks the moment your dataset changes — and how hash-based splitting with MurmurHash3 gives you deterministic, stable assignments that survive new data.

A short section of 'The Mountain Is You' got me thinking about how often we let the fear of failure stop us from starting. But not starting isn't standing still — the world keeps moving, with or without you.

In this third post of the Learning Machines series, let's examine online versus batch learning, instance-based versus model-based generalization, and write our first linear regression model in scikit-learn.