Facebook has released HiPlot, a data visualization tool that can help researchers discover correlations and patterns in high-dimensional data. Machine learning models can have dozens of hyperparameters, such as the model’s learning rate, as well as thousands of data points and parameters, such as the height, weight, and heart rate of an individual. HiPlot helps researchers understand the influence of hyperparameters in complex models by plotting relationships on parallel axes.
Discovering Patterns in High-Dimensional Data
Michael McLaughlin is a research analyst at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
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