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From EDA to features: encoding, scaling, and the leakage traps in between
A machine learning model can achieve a perfect validation score and still be completely useless in production.
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Dimensionality reduction for seeing: PCA, t-SNE, and UMAP compared honestly
A dataset with 64 features cannot be plotted directly on a two-dimensional screen. But a machine learning engineer can transform those 64 features into two coordinates and produce a surprisingly beaut
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Simpson’s paradox: when your aggregate answer is exactly wrong
Imagine you are a data analyst at an e-commerce company. Your team uses two shipping carriers, ExpressSwift and BudgetFlow, and management wants to know which one delivers packages on time more reliab
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Regularizing a boosted model: reg_lambda, reg_alpha, gamma, and the constraints that matter
XGBoost does not regularize a tree in quite the same way that ridge regression or Lasso regularizes a linear model. A standard tree booster does not have one coefficient per input feature. Instead, it
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Detecting data drift in production with the K–S test and PSI
A machine-learning model can be perfectly healthy from an infrastructure perspective and still become increasingly wrong.
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Chi-squared tests for categorical data: independence and goodness-of-fit
Many important questions in data analysis involve categories rather than numerical measurements:
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Regularization in practice: telling underfitting from overfitting and choosing the strength
A machine learning model can fail in two opposite ways.
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Bootstrap and permutation tests: confidence intervals and p-values without a formula
You have two versions of an API endpoint. The new version appears faster, but its latency distribution is skewed, a handful of requests take much longer than the rest, and the median matters more to y
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Rules vs model bake-off: measuring when learned models actually earn their complexity
A machine learning model achieves 77.5% accuracy. A handful of hand-written rules achieves 76.0%. The model wins the benchmark.
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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.