![]() ![]() ![]() You can read more about this problem on the UCI Machine Learning Repository page for the Ionosphere dataset. ![]() It is comprised of 16 pairs of real-valued radar signals (34 attributes) and a single class attribute with two values: good and bad radar returns. The problem is to predict the presence (or not) of free electron structure in the ionosphere given radar signals. The Ionosphere Dataset is a classic machine learning dataset. Open the “data“directory and choose the “ionosphere.arff” dataset.In the “Datasets” select click the “Add new…” button.Let’s start out by selecting the dataset. It is a “Classification” type problem and each algorithm + dataset combination is run 10 times (iteration control). The experiment is configured to use Cross Validation with 10 folds. The experimenter configures the test options for you with sensible defaults. Design ExperimentĬlick the “New” button to create a new experiment configuration. Take my free 14-day email course and discover how to use the platform step-by-step.Ĭlick to sign-up and also get a free PDF Ebook version of the course. Need more help with Weka for Machine Learning? The Weka Experimenter allows you to design your own experiments of running algorithms on datasets, run the experiments and analyze the results. The Weka GUI Chooser lets you choose one of the Explorer, Experimenter, KnowledgeExplorer and the Simple CLI (command line interface).Ĭlick the “Experimenter” button to launch the Weka Experimenter. This may involve finding it in program launcher or double clicking on the weka.jar file. If you are interested in machine learning, then I know you can figure out how to download and install software into your own computer. I’m on a Mac myself, and like everything else on Mac, Weka just works out of the box. You may already have Java installed and if not, there are versions of Weka listed on the download page (for Windows) that include Java and will install it for you. Visit the Weka Download page and locate a version of Weka suitable for your computer (Windows, Mac or Linux). 7.0.2 Finally Bring The Machine Learning To Your Own Projects.7.0.1 Develop Your Own Models in Minutes. ![]() 7 Discover Machine Learning Without The Code!.2.1 Need more help with Weka for Machine Learning?. ![]()
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January 2023
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