Optimize LightGBM Hyperparameters

The skill builds LightGBM hyperparameter tuning scripts around Optuna, structured as a reusable LightGBMTuner class that takes the task type (binary/multiclass/regression) and configures the right objective, metric and evaluation function automatically. It tunes parameters in priority order - primary (num_leaves, learning_rate, feature_fraction, bagging_fraction), secondary regularization (min_data_in_leaf, lambda_l1, lambda_l2, min_gain_to_split), then advanced (max_depth, bagging_freq, max_bin) - following a search-strategy hierarchy of coarse grid search, Bayesian optimization, random search and successive halving.

Use it to write a tuning script that cross-validates each trial properly - StratifiedKFold for classification, KFold for regression, TimeSeriesSplit when the data is temporal - and trains each fold with early stopping rather than a fixed number of rounds. A minimal usage pattern:

# Initialize tuner
tuner = LightGBMTuner(X_train, y_train, task_type='binary', n_trials=200)

# Run optimization
best_params = tuner.tune_with_monitoring()

# Create production model
production_params = tuner.create_production_config(best_params)
final_model = lgb.train(production_params, train_data, num_boost_round=1000)

It is not a general AutoML tool - it is scoped specifically to LightGBM's own parameter space and Optuna as the search backend.

Input is the training feature matrix and labels, plus the task type and trial budget. Output is a best-parameter dictionary from the Optuna study, reachable through several strategies: a single-stage objective(), a multi_stage_tuning() pipeline that optimizes tree structure, then regularization, then fine-tuning as separate Optuna studies, categorical-aware tuning that adds cat_smooth/cat_l2/max_cat_threshold, and a monitored/pruned variant using optuna.pruners.MedianPruner that reports intermediate CV-fold scores and raises optuna.TrialPruned() on unpromising trials.

It sits on top of lightgbm, optuna, and scikit-learn's cross-validation splitters (StratifiedKFold, KFold, TimeSeriesSplit). The production-config step is a distinct final stage: it forces column- or row-wise histogram building based on dataset size, fixes max_bin/histogram_pool_size for memory, and pins deterministic: True with fixed seeds (seed, bagging_seed, feature_fraction_seed) for reproducible inference.

Its closing recommendations apply regardless of which strategy is used: always validate the final model on a held-out test set the tuning process never saw, monitor for overfitting throughout tuning rather than only at the end, use feature-importance analysis to sanity-check which parameters are doing the work, and consider ensembling for applications where a single tuned model isn't reliable enough.

ML engineers who would otherwise hand-write a fresh search loop from scratch for every new model and would rather start from one reusable, already-tested class covering the ground work - the boilerplate of wiring up a search library, splitting data correctly, and turning a result into something deployable.