This node uses the model as generated by a Correlation node to determine which columns are redundant (i.e. correlated) and filter…

This node induces a classification decision tree in main memory. The target attribute must be nominal. The other attributes used …

This rule learner* learns a Fuzzy Rule Model on labeled numeric data using Mixed Fuzzy Rule Formation as the underlying training …

This view takes a hierarchical cluster tree and the same input table, that has been used for the creating the clustering and visu…

Filters out double-compatible columns, whose variance is below a user defined threshold. Columns with low variance are likely to …

Based on a trained MultiLayerPerceptron-model given at the model inport of this node, the expected output values are computed. If…

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of which attribute and the…

The association rule learner* searches for frequent itemsets meeting the user-defined minimum support criterion and, optionally, …

This rule learner* uses the Apriori (Agrawal et al. 1993) algorithm implemented by Christian Borgelt. The following description h…

This is the end node of a backward feature elimination loop. It compares the target column with the prediction column set in the …

This node takes a model built with a feature elimination loop and lets you choose the column you want to include in the output ta…

This is the start node of a backward feature elimination loop. The first iteration of the loop is executed with all input columns…

This is the start node of a backward feature elimination loop. The first iteration of the loop is executed with all input columns…

Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs…

Together with the corresponding loop end node a boosting loop can be constructed. It repeatedly trains simple models and weights …

Together with the corresponding loop start node a boosting prediction loop can be constructed. It uses the weighted models an cre…

Together with the corresponding loop end node a boosting prediction loop can be constructed. It uses the weighted models an creat…

Converts a single table cell into a model output.

This node analyses documents and extracts relevant keywords using cooccurrence statistics as described in "Keyword extraction fro…

This node assigns new data to an existing set of prototypes, which are obtained e.g. by a k-means clustering. Each data point is …