Xgboost is a powerful gradient boosting framework that can be used to train Machine Learning models. It is important to select optimal number of trees in the model during the training. Too small number of trees will result in underfitting. On the other hand, too large number of trees will result in overfitting. How to find the optimal number of trees? You can use an early stopping. We are using Xgboost in version 2.0.0 in this article. Please update xgboost package if you have older version.

Xgboost is a powerful gradient boosting framework. It provides interfaces in many languages: Python, R, Java, C++, Juila, Perl, and Scala. In this post, I will show you how to save and load Xgboost models in Python. The Xgboost provides several Python API types, that can be a source of confusion at the beginning of the Machine Learning journey. I will try to show different ways for saving and loading the Xgboost models, and show which one is the safest.

Xgboost is a gradient boosting library. It provides parallel boosting trees algorithm that can solve Machine Learning tasks. It is available in many languages, like: C++, Java, Python, R, Julia, Scala. In this post, I will show you how to get feature importance from Xgboost model in Python. In this example, I will use boston dataset availabe in scikit-learn pacakge (a regression task).