After successful execution, the tool generates the summary and presents the algorithmвЂ™s effectiveness based on different parameters: To begin with, this classifier is the implementation of the 0-R classifier and allows batch processing. LetвЂ™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. Find datasets covering pick-up/drop-off times and locations, trip distances, fares, rate and payment types, passenger counts, and more. Percentage split вЂ“ the classifier will be judged on a specific percentage of data Sample dataset: Take your pick This is a weirdly fascinating onesince 2009, the NYC Taxi and Limousine Commission has been accruing transport data from across New York City.Cross-validation Folds вЂ“ assessment of the classifier based on cross-validation using the number of provided folds.Supplied test set вЂ“ evaluates the classifier based on a separate test set.Use training set вЂ“ the classifier will be tested on the same training set.The classic examples of classification are: declaring a brain tumor as вЂњmalignantвЂќ or вЂњbenignвЂќ or assigning an email to вЂњspamвЂќ or вЂњnot_spamвЂќ class.ДЄfter 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.Д¬lassification 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 вЂ“ contains algorithms that use decision trees, such as J48, RandomForestД®ach algorithm comes up with configuration params such as batchSize, debug, etc.rules вЂ“ combines algorithms that use rules such as OneR, ZeroR.misc вЂ“ miscellaneous algorithms that do not fit any of given category.

meta вЂ“ consists of those algorithms that use or integrate multiple algorithms for their work like Stacking, Bagging.lazy вЂ“ covers all algorithms that use lazy learning similar to KStar, LWL.functions вЂ“ comprises the algorithms that estimate a function, including Linear Regression.bayes вЂ“ consists of algorithms based on Bayes theorem like Naive Bayes.LetвЂ™s look at those groups and their core nature: These are available under the вЂњExplorerвЂќ 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.

Install weka cannot find any datasets