Boost XGBoost Performance: Easy Hyperparameter Optimization (Code)

XGBoost (Extreme Gradient Boosting) is a powerful and efficient machine learning algorithm that is based on the principles of gradient boosting. It is widely used for classification and regression tasks due to its high performance and accuracy.
Tuning XGBoost involves adjusting its hyperparameters to optimize its performance on a specific dataset. When tuning these hyperparameters, it's common to use techniques like grid search or randomized search. Cross-validation is essential to evaluate the model's performance for different parameter combinations. In this video, we look at a new optimal approach to tune the XGboost classifier.
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[@Sameerkang-xz3ck]

Amazing piece of work with Mango

[@MachineLearningSchool]

Code: colab.research.google.com/github/ARM-software/mango/blob/main/examples/Xgboost_XGBClassifier.ipynb

[@simarchannel1]

Waiting for more videos

[@MachineLearningSchool]

👍

[@nesltime3738]

Thanks 🙏

[@nesltime3738]

I have been searching for this

[@mandeepusakaur]

Xgboost is complex to tune. This looks promising

[@gurdeepsingh-kn9ut]

Thanks for this. xgboost is so hard to tune manually