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1. Optuna: Automate Hyperparameter Search

   Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.

   2025-04-22 Tags: hyperparameter, optimization, machine learning, automation, tensorflow, keras, sprinkler, xgboost, lightgbm, optuna by klotz
2. XGBoost: Intro, Step-by-Step Implementation, and Performance Comparison

   - Extreme Gradient Boosting: A quick and reliable regressor and classifier
   - Summary: LightGBM is faster and better though XGBoost is close

   2023-09-29 Tags: machine learning, gofml, accuracy, precision, recall, xgboost, random forest, svm, k-nn, lightgbm, catboost, gradientboosting, adaboost by klotz
3. Microsoft/LightGBM: A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK

   2019-03-25 Tags: decision tree, microsoft, lightgbm by klotz

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