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Ensemble Models: Bagging and Boosting

Click to learn more about author Rosaria Silipo. Ensemble models combine multiple learning algorithms to improve the predictive performance of each algorithm alone. There are two main strategies to ensemble models — bagging and boosting — and many examples of predefined ensemble algorithms. Bootstrap aggregation, or bagging, is an ensemble meta-learning technique that trains many […]

From a Single Decision Tree to a Random Forest

Click to learn more about author Rosaria Silipo. The co-author of this column was Kathrin Melcher. Decision trees are a set of very popular supervised classification algorithms. They are very popular for a few reasons: They perform quite well on classification problems, the decisional path is relatively easy to interpret, and the algorithm to build […]

Automated Machine Learning: Just How Much?

Click to learn more about author Rosaria Silipo. An interview with three data scientists and guided automation experts There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz.. to ask a few questions […]

Fraud Detection Using a Neural Autoencoder

Click to learn more about author Rosaria Silipo. The co-authors of this column were Kathrin Melcher and Maarit Widmann. The Fraud Detection Problem Fraud detection belongs to the more general class of problems — the anomaly detection. Anomaly is a generic, not domain-specific, concept. It refers to any exceptional or unexpected event in the data, […]

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