Introduction to gradient-boosted trees and XGBoost hyperparameters tuning (with python)

January, 2017


XGBoost model is a supervised machine learning algorithm that takes in the training data and constructs a model that predicts the outcome of new data instances.

XGBoost has gained a lot of popularity in the machine learning community due to its ability to train versatile model with speed and quality performance. It’s an implementation of gradient boosted decision trees which are constructed for speed and performance.

For an in-depth introduction visit this link for more technical details.

Here we cover a gentle introduction of XGBoost model using python scikit-learn package.

By the end of this tutorial you will have learnt:

XGBoost installation for python use

XGBoost data preparation and model training

XGBoost model prediction

XGBoost hyperparameters tuning

Please continue to read the original post Introduction to gradient-boosted trees and XGBoost hyperparameters tuning (with python) on my old blog.

Introduction to gradient-boosted trees and XGBoost hyperparameters tuning (with python) - January 1, 2017 - lorenzo toscano
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