Regression Model and Prediction

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 algorithm uses gradient boosting to train ensemble models. Gradient boosting is a powerful machine learning technique that uses an ensemble of weak prediction models, typically decision trees, to create a more accurate prediction model.

H2OXGBoostEstimator is based on the XGBoost library, which is a popular implementation of gradient boosting. The key advantage of H2OXGBoostEstimator is that it can handle a large number of features and data points, making it well suited for big data problems. It also ...