Decision Tree Predictor

This node uses an existing decision tree (passed in through the model port) to predict the class value for new patterns. The Node can be configured as follows:

Options

Number of patterns for HiLite: Determines the maximum number of patterns the tree will store to support HiLiting.
Change prediction column name: When set, you can change the name of the prediction column.
Prediction Column: The possibly overridden column name for the predicted column. (The default is: Prediction (trainingColumn).)
Append columns with normalized class distribution: Shows the normalized class distribution for each prediction.
Suffix for probability columns: Suffix for the normalized distribution columns. Their names are like: P (trainingColumn=value).

Input Ports

: A previously learned decision tree model
: Input data to classify

Output Ports

: The input table with one column added containing the classification and the probabilities depending on the options

Popular Predecessors

Popular Successors

Views

Decision Tree View: The decision tree as given in the model port along with the classified data. The tree can be expanded and collapsed with the plus/minus signs.
Decision Tree View (simple): The decision tree as given in the model port along with the classified data.

Workflows

Further Links

KNIME E-Learning Course: Decision Treewww.knime.com/knime-introductory-course/chapter6/section3/decision-tree

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.