mljourney Cross-validation gives you a reliable estimate of how an XGBoost model will perform on unseen data. A single train/test split is noisy — performance varies significantly depending on which samples happen to end up in the test set. Cross-validation reduces that variance by averaging over multiple splits. This guide covers how to do it correctly … Read more
Wrapping XGBoost in a scikit-learn pipeline solves a problem that bites almost every ML project eventually: training-serving skew. When preprocessing steps live outside the model, it is easy for inference code to apply them slightly differently — wrong scaler, missing imputer, column order mismatch — producing silent errors that corrupt predictions without raising exceptions. A … Read more
Saving and loading XGBoost models sounds trivial — and mostly it is — but there are enough format options, version compatibility nuances, and post-load gotchas (particularly around early stopping) to make it worth covering carefully. This guide covers every serialisation format XGBoost supports, when to use each, and what to watch out for. Format Overview … Read more
Feature importance scores built into XGBoost — gain, weight, cover — tell you which features the model used, but not how or in which direction. SHAP values do both: they assign each feature a contribution to each individual prediction, with sign indicating direction and magnitude indicating impact. The result is a local explanation you can … Read more
XGBoost is not a time series model — it has no built-in understanding of temporal order, seasonality, or autocorrelation. But it becomes a powerful time series forecaster once you add that structure through feature engineering. The approach is to transform a forecasting problem into a supervised regression problem: create lag features, rolling statistics, and calendar … Read more
Imbalanced classification — fraud detection, medical diagnosis, churn prediction, rare event detection — is where default model settings fail most predictably. A model that predicts the majority class for every sample achieves 99% accuracy on a 1:99 dataset while being completely useless. LightGBM has several built-in mechanisms for imbalanced data, and combining them with the … Read more
XGBoost’s GPU training can cut training time by 5–50x depending on dataset size and hardware. On a dataset with a million rows the difference between CPU and GPU training is often the difference between waiting 20 minutes per Optuna trial and waiting 45 seconds. This guide covers how to enable GPU training, what the parameter … Read more
Early stopping is one of the most useful features in XGBoost — and one of the most frequently misconfigured. It prevents overfitting by halting training when the validation metric stops improving, and it eliminates the need to manually search for the right number of trees. This guide covers how to use it correctly, the gotchas … Read more
LightGBM hyperparameter tuning guide covering num_leaves, min_child_samples, subsample, regularisation, and Optuna search.
CatBoost quietly became one of the best gradient boosting libraries available. It handles categorical features natively without preprocessing, trains fast on GPU, and consistently outperforms XGBoost and LightGBM on datasets with lots of categorical data — which describes most real-world tabular datasets. If you have been manually one-hot encoding categoricals before throwing them at XGBoost, … Read more
