Wolfram Data Framework Semantic framework for real-world data. The tool saves the results in the Result list (highlighted blue). After successful execution, the tool generates the summary and presents the algorithm’s effectiveness based on different parameters: To begin with, this classifier is the implementation of the 0-R classifier and allows batch processing. Let’s apply ZeroR classifier to the dataset. Other than these, we can also use more test options such as Preserve order for % split, Output source code, etc.
Percentage split – the classifier will be judged on a specific percentage of data. Cross-validation Folds – assessment of the classifier based on cross-validation using the number of provided folds. Supplied test set – evaluates the classifier based on a separate test set. Use training set – the classifier will be tested on the same training set. The classic examples of classification are: declaring a brain tumor as “malignant” or “benign” or assigning an email to “spam” or “not_spam” class.Īfter the selection of the desired classifier, we select test options for the training set. These configurations can be editable once the algorithm is selected to use.Ĭlassification is one of the essential functions in machine learning, where we assign classes or categories to items. Some of the configuration params are common across all the algorithms, while some are specific. trees – contains algorithms that use decision trees, such as J48, RandomForestĮach algorithm comes up with configuration params such as batchSize, debug, etc. rules – combines algorithms that use rules such as OneR, ZeroR. misc – miscellaneous algorithms that do not fit any of given category. meta – consists of those algorithms that use or integrate multiple algorithms for their work like Stacking, Bagging. lazy – covers all algorithms that use lazy learning similar to KStar, LWL. functions – comprises the algorithms that estimate a function, including Linear Regression. bayes – consists of algorithms based on Bayes theorem like Naive Bayes. Let’s look at those groups and their core nature: These are available under the “Explorer” tab of the WEKA. All the algorithms, because of their core nature, are divided into several groups. WEKA provides ample amounts of algorithms for machine learning tasks.
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