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Feature selection and SHAP: knowing which features matter and proving it
A model can achieve excellent predictive accuracy and still be fundamentally wrong about which features are useful.
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Metrics that match the problem: ROC-AUC vs F1 vs MAE vs RMSE, and when accuracy lies
A machine learning model achieves 99% accuracy . Its dashboard looks impressive. Stakeholders are satisfied, and the team is preparing to deploy it.
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Running models locally: Ollama, llama.cpp, and ONNX Runtime on your own hardware
Running an AI model locally means executing its computations on hardware you control instead of sending every request to a remote inference provider.
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Hyperparameter search done right: GridSearchCV, RandomizedSearchCV, and Optuna with CV built in
Hyperparameter tuning is easy to get started with and surprisingly easy to get wrong.
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Which model when: a practical decision playbook for tabular, text, images, and small data
Model selection gets easier when you stop asking, “What is the best machine-learning model?” and ask a more useful set of questions:
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sklearn Pipeline and ColumnTransformer: preprocessing that cannot leak
This tutorial targets scikit-learn 1.9.0 , the current stable release as of August 2026. The current documentation describes Pipeline as the mechanism for assembling preprocessing and estimators so th
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Rolling windows and lag features: time series into supervised learning without leaking the future
A time series becomes a supervised-learning table when each row represents a prediction decision:
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Scaling and normalization: StandardScaler vs MinMaxScaler vs RobustScaler, and the models that don’t care
Suppose a dataset contains two useful features:
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Encoders shootout: one-hot, ordinal, target, and hashing — when each wins and when it leaks
Categorical features are everywhere in tabular machine learning: