Classification with H2O
Explore classification models in H2O by understanding binary and multiclass classification techniques. Learn to apply various algorithms including GLMs, gradient boosting, deep learning, and random forests. Gain practical experience with data exploration, model training, tuning, and leveraging H2O AutoML to automate model selection using datasets such as Lending Club loans.
Introduction to classification models
Classification models are machine learning algorithms that learn to classify data into predefined categories or classes based on patterns and relationships within the data. They use various mathematical techniques such as statistical algorithms, decision trees, and neural networks. In mathematical terms, they map input variables (x) to output variables (y) and return an item from a discrete set of options, making a “true or false” decision or picking one option out of a few. This relationship is most commonly represented as:
where