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. Problems with variable length series are listed as length 0.WEKA provides ample amounts of algorithms for machine learning tasks. More information on the datasets is given below. To see how accurate different classifiers are on these data see the results page. These files provide a simple list of the data characteristics for univariate and These are fairly unintuitive, so we have provided an overview of this and other basic features of loading data and building classifiers Series in ARFF we take advantage of relational attributes. Lists of the data, including which are unequal length, can be found here.ĭetails on loading sktime data with the Python package are here. Sktime formatted ts files (about 1.5 GB). Multivariate Weka formatted ARFF files (and. Univariate sktime formatted ts files (about 300 MB). Univariate Weka formatted ARFF files and. Weka does not allow for unequal length series, so the unequal length problems are all padded with missing values.

The univariate and multivariate classification problems are available in three formats: Weka ARFF, simple text files and sktime ts format.

Weka arff file format