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This node reads Excel files (xlsx, xlsm, xlsb, and xls format). It can read a single or multiple files at the same time, however reading only one sheet per file. The supported Excel types that can be read in are string, number, boolean, date, and tim

Converts numbers in a column (or a set of columns) to strings. Note that for an advanced configuration, such as rounding or representation in scientific notification you can also use the "Round Double" node.

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is built with a different set of rows (records) and for each split within a tree a randomly chosen set of columns (describing attributes) i

Predicts patterns by aggregating the predictions of the individual trees in a random forest model.Random Forests is a registered trademark of Minitab, LLC and is used with Minitab’s permission.

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

The node creates a Bayesian model from the given training data. It calculates the number of rows per attribute value per class for nominal attributes and the Gaussian distribution for numerical attributes. The created model could be used in the naive

Predicts the response using a logistic regression model. The node needs to be connected to a logistic regression node model and some test data. It is only executable if the test data contains the columns that are used by the learner model. This node

Predicts the class per row based on the learned model. The class probability is the product of the probability per attribute and the probability of the class attribute itself.The probability for nominal values is the number of occurrences of the cla

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c

The input table is split row-wise into two partitions, for instance into a train and test data set. The two partitions are available at the two output ports.

Performs a multinomial logistic regression. Select in the dialog a target column (combo box on top), i.e. the response. The solver combo box allows you to select which solver should be used for the problem (see below for details on the different solv

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c

Compares two columns by their attribute value pairs and shows the confusion matrix, i.e. how many rows of a given attribute and its classification match. The dialog allows you to select two columns for comparison; the values from the first selected c