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 hiliting

Determines the maximum number of patterns the tree will store to support hiliting.

Custom prediction column name

Allows you to specify a customized name for the prediction column that is appended to the input table. If not checked, Prediction(target) (where target is the name of the target column of the provided regression model) is used as default.

Append columns with normalized class distribution

If selected, a column is appended for each class instance with the normalized probability of this row being a member of this class. The probability columns will have names like: P(targetColumn=value).

Probability column suffix

Suffix for the probability columns.

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

• Credit Scoring Model(diksha kumari)KNIME Hub
• !Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub
• (test) credit score modelKNIME Hub
• (Titanic)_ML_StepsKNIME Hub
• [Latest] Consolidated 3 Mar 1210KNIME Hub
• Show all 3332 workflows

Links

• KNIME E-Learning Course (see "Decision Tree" section)

  www.knime.com/learning
• Help!- Churn for Bank Customers Analyzingby gonhaddock on 2022-05-18

  forum.knime.com/p/157353

Developers