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In this session, we focus on how using connected features improves the accuracy, precision and recall of machine learning models. We discuss how graph algorithms provide more predictive features and aid in feature selection that reduces overfitting.
We also look at a link prediction example that highlights how graph-based features infer collaboration with measurable improvement.
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About the Author
Mark Needham & Amy E. Hodler , Neo4j
Amy is the Analytics and AI Program Manager at Neo4j. She believes a thriving graph ecosystem is essential to catalyze new types of insights. Accordingly, she helps ensure Neo4j partners are successful. In her career, Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft, and Hewlett-Packard (HP). She most recently comes from Cray Inc., where she was the analytics and artificial intelligence market manager.Amy has a love for science and art with an extreme fascination for complexity science and graph theory. When the weather is good, you’re likely to find her cycling the passes in beautiful Eastern Washington.