by Gabriel Tam | Mar 21, 2026 | BTS Consulting
Attribute importance is a machine learning technique used to determine which variables in a dataset have the greatest influence on a given outcome. While many datasets contain a large number of features, not all of them contribute equally to predictions. Attribute...
by Gabriel Tam | Mar 14, 2026 | BTS Consulting
Clustering is an unsupervised machine learning technique that helps find natural groupings within a dataset. Instead of trying to predict a specific outcome, clustering focuses on identifying records that are similar to each other. Data points that share similar...
by Gabriel Tam | Mar 7, 2026 | BTS Consulting
Association rules are a type of unsupervised machine learning technique that helps find relationships between items in a dataset. Instead of predicting a specific outcome, the goal is to spot patterns that show how certain items or events tend to occur together. These...
by Gabriel Tam | Feb 28, 2026 | BTS Consulting
Anomaly detection is an unsupervised machine learning technique that identifies observations which differ significantly from most of the data. Unlike supervised methods such as classification or regression, anomaly detection does not need labelled outcomes. Instead,...
by Gabriel Tam | Feb 21, 2026 | BTS Consulting
Previously, we discussed about what feature extraction is and the algorithms that support it. In this post, the focus shifts to why feature extraction is so useful in real world machine learning scenarios. Drawing from Oracle Machine Learning documentation, feature...
by Gabriel Tam | Feb 14, 2026 | BTS Consulting
Machine learning projects are most successful when they follow a structured and repeatable process. Oracle Machine Learning adopts a well defined lifecycle that guides projects from the initial business problem through to deployment in a production environment. This...
