OmbuLabs Blog

Welcome to the third article in our Machine Learning with Ruby series!

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. We then looked into our first ensemble model technique, Random Forests, in Machine Learning: An Introduction to Random Forests. It is a good idea to review that article before diving into this one.

Random Forests are great for a wide variety of cases, but there are also situations where they don’t perform quite as well. In this article we’ll take a look at another popular tree-based ensemble model: Gradient Boosting.

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. Decision trees are very flexible and are a good tool for simple classification, but they are often not enough when it comes to real-world scenarios.

When dealing with large and complex data, or when dealing with data with a significant amount of noise, we need something more powerful. That’s where ensemble models come into play. Ensemble models combine a number of weak learners to build a strong model, with increased accuracy and robustness. Ensembles also help manage and reduce bias and overfitting.

In this article, we’ll cover a very popular tree-based ensemble model: Random Forest.

In our previous article, Machine Learning Aided Time Tracking Review: A Business Case we introduced the business case behind Pecas, an internal tool designed to help us analyse and classify time tracking entries as valid or invalid.

This series will walk through the process of shaping the original problem as a machine learning problem and building the Pecas machine learning model and the Slackbot that makes its connection with Slack.

In this first article, we’ll talk through shaping the problem as a machine learning problem and gathering the data available to analyse and process.