Introduction
Analytic Solver Data Science includes comprehensive, powerful support for data science and machine learning. Using these tools, you can "train" or fit your data to a wide range of statistical and machine learning models: Classification and regression trees, neural networks, linear and logistic regression, discriminant analysis, naïve Bayes, k-nearest neighbors and more. But the task of choosing and comparing these models, and selecting parameters for each one was up to you.
With the new Find Best Model options in V2021.5, you can automate this work as well! Find Best Model uses methods similar to those in (expensive high-end) tools like DataRobot and RapidMiner, to automatically choose types of ML models and their parameters, validate and compare them according to criteria that you choose, and deliver the model that best fits your data.
Continue reading to discover how to utilize Find Best Model though an easy-to-follow example. For more information on how to run each classification or regression learner independently of Find Best Model, see each learner's topic in this online help.