Improving Machine Learning Predictions Using Graph Algorithms

Presented by

Amy Hodler and Mark Needham, Neo4j

About this talk

Graph enhancements to AI and machine learning are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.

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Neo4j
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Today’s businesses need to generate real-time, valuable insights from their existing data. In this case, the relationships between data points matter more than the individual points themselves. Industry leads are turning to graph databases which treat those valuable relationships (connections) as first-class entities. Neo4j is the world's leading graph database, with native graph storage and processing.