Regression Model and Prediction

Explore training and tuning H2OXGBoost regression models within the H2O framework. Understand key hyperparameters to improve model performance and discover how to generate reliable predictions on airline fare data, creating an end-to-end ML pipeline.

We'll cover the following...

Regression model: H2OXGBoostEstimator
Tune the H2OXGBoostEstimator model
Get predictions

H2OXGBoost is an implementation of the XGBoost algorithm within the H2O framework. It’s based on the tree-based gradient boosting algorithm that uses an ensemble of weak learners to produce a final model. H2OXGBoost is well known for its high performance and scalability on various machine learning tasks.

Regression model: H2OXGBoostEstimator

In this lesson, we’re going to train the H2OXGBoostEstimator regression model to predict flight fares from the airline dataset. The H2OXGBoostEstimator ...

1.Introduction to Machine Learning

2.Supervised Learning: Regression Models with H2O

3.Supervised Learning: Classification Models with H2O

4.Unsupervised Learning: Clustering with H2O

5.Unsupervised Learning: Anomaly Detection with H2O

6.Closing Notes

7.Appendix

Regression Model and Prediction

Regression model: H2OXGBoostEstimator