Amy Hodler Presenting “Improving ML Predictions with Connected Feature Extraction”

UPDATED: Video Added from the Conference!

Introducing Amy Hodler > @amyhodler < presenting “Improving ML Predictions with Connected Feature Extraction”.

Amy is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.

One of the most practical ways to improve our machine learning predictions right away is by using graphs for connected features. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting.

In this session, you’ll hear about a link prediction example for collaboration with tips on training and evaluating a model using Neo4j and Spark. We’ll compare several models and show measurable improvements in accuracy, precision, and recall by folding in graph-based features.

Come check out Amy Hodler’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!