![]() Identifying features that have predictive power yet are not redundant.Today, I’m going to show you how you can use AutoML to automate one (or all) of the following phases. Achieving good performance requires significant effort: choosing a model (and then choosing a different model), identifying and extracting features, and even adding more or better training data.Įven experienced machine learning experts know it’s a lot of trial and error to finally arrive at a performant machine learning model. Step 3: Obtain Optimized Model In One-Stepīuilding good machine learning models is an iterative process (as shown in the figure below).Step 1: Extract Initial Features Automatically by Applying Wavelet Scattering. ![]() Example Application: A Human Activity Classifier.Software requirements: Executing this script requires MATLAB version R2020a. Instead of requiring significant machine learning expertise and following a lengthy iterative optimization process, AutoML delivers good models in a few steps. ![]() In today’s post, Bernhard discusses how obtaining optimized machine learning models gets a lot easier and faster by applying AutoML. Prior to joining MathWorks Bernhard led analyst teams and developed methods applying analytics to optimizing the delivery of customer service in call centers. Today I’d like to introduce Bernhard Suhm who works as Product Manager for Machine Learning here at MathWorks. Or: Optimized Machine Learning without the expertise
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