![]() ![]() The algorithm is tested by holding out examples from one fold at a time the model is induced from other folds and examples from the held out fold are classified. Cross-validation splits the data into a given number of folds (usually 5 or 10).The widget supports various sampling methods.The Learner signal has an uncommon property: it can be connected to more than one widget to test multiple learners with the same procedures. ![]() Second, it outputs evaluation results, which can be used by other widgets for analyzing the performance of classifiers, such as ROC Analysis or Confusion Matrix. First, it shows a table with different classifier performance measures, such as classification accuracy and area under the curve. Different sampling schemes are available, including using separate test data.
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