This is the concluding post in a series of four posts. In the previous post, Going Deeper with Tribuo, we created a Random Forest model that was trained to predict the quality of red wine based on the values of eleven given characteristics. This time, we will use this model to create a REST service that will respond with a…
Category: Machine Learning Basics
Going Deeper with Tribuo—Regression, Provenance and Model Serialization
This is the third post in a series of four posts. In the previous post, First Tribuo Example, we saw how to implement a wine quality classifier with Tribuo, a Java-based Machine Learning library. This time, we will use the same dataset to demonstrate how to implement a regressor, and along the way discover a few more capabilities of Tribuo.…
First Tribuo Example—Wine Quality Classifier
This is the second post in a series of four posts. As we saw in Introduction to Supervised Learning, one of the basic machine learning tasks is classification, where we need to map a set of inputs (usually referred to as ‘features’) into two or more categories (or ‘classes’). While typically data for such tasks needs to be collected and…
Introduction to Oracle Tribuo
This is the opening post in a series of four posts. Oracle Tribuo is a Java based, open-source, general purpose machine learning library. It provides tools for various machine learning tasks, such as classification, regression and clustering, as well as natural language processing (NLP). In addition, Tribuo enables Java programs to use models that were trained by Python libraries, such as…
Enhancing Machine Learning Models using Genetic Algorithms with Python
In this post, you will learn about supervised machine learning, and find out how genetic algorithms can be used to improve the performance of machine learning models by selecting the best subset of features from the provided input data. Along the way, you will get acquainted with the real-life ‘Zoo’ dataset and discover how to utilize Python code to create a…