Stunting

Use this node to read CSV files into your workflow. The node will produce a data table with numbers and types of columns guessed automatically.

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.

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.

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.

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.

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