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Categorical features and missing values in XGBoost: the native path vs one-hot
XGBoost can learn from missing numeric values and categorical columns without forcing every project through an impute-then-one-hot pipeline. The real decision is not whether native support exists; it
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Tuning XGBoost without fooling yourself: early stopping, learning rate, and the parameters that matter
XGBoost tuning becomes dangerous when optimization and evaluation blur together. A workflow can try dozens of configurations, stop each run at its best validation round, select the lowest validation e
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Gradient boosting from the ground up: why XGBoost and LightGBM still rule tabular data in 2026
Tabular ML has an unfashionable truth: the best model is often not the newest architecture.
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Polars vs pandas: the DataFrame rewrite that changed the default
For years, the default choice for tabular Python was automatic: import pandas and start manipulating rows. That default has changed.
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Topics Everyone Is Talking About No398
Gemini Robotics 2 brings whole body intelligence to robots • Google will expand age checks on Android worldwide till the end of the year • Why is everyone trying to build a solid-state battery? • deskhop: Fast Desktop Switching Device • C float-to-int conversion can be undefined behavior…
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Dynamic batching: the single biggest throughput lever in model serving
A model server processing one request at a time pays fixed costs for every execution:
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Decision trees: how they split, why they overfit, and how to read one
A decision tree turns prediction into a sequence of readable questions:
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Outliers: detect, understand, and only then decide to delete
An outlier is a record that looks unusual relative to other observations. That definition says nothing about whether the record is wrong.
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Activation functions in 2026: why ReLU won and what’s replacing it
ReLU remains the cheapest dependable baseline, but modern transformer feed-forward blocks increasingly favor smooth, gated designs—especially GELU, SiLU, and SwiGLU—because they preserve a gradient pa
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Feature stores and reproducible training pipelines
A model is not reproducible merely because its training script has a fixed random seed. You must also reproduce the exact feature definitions, historical values, timestamps, dependency graph, and mode