サクサク読めて、アプリ限定の機能も多数！
トップへ戻る
新年度はじまる
lightgbm.readthedocs.io
Parameters Tuning This page contains parameters tuning guides for different scenarios. List of other helpful links Parameters Python API FLAML for automated hyperparameter tuning Optuna for automated hyperparameter tuning Tune Parameters for the Leaf-wise (Best-first) Tree LightGBM uses the leaf-wise tree growth algorithm, while many other popular tools use depth-wise tree growth. Compared with
Welcome to LightGBM’s documentation! LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. Lower memory usage. Better accuracy. Support of parallel, distributed, and GPU learning. Capable of handling large-scale data. For more details, please ref
このページを最初にブックマークしてみませんか？
『lightgbm.readthedocs.io』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
