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BFTree (3.6)

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Options

Class column

Choose the column that contains the target variable.

Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Missing values, Nominal class, Binary class] Dependencies: [] min # Instance: 1

Classifier Options

S: Random number seed. (default 1)

D: If set, classifier is run in debug mode and may output additional info to the console

P: The pruning strategy. (default: POSTPRUNED)

M: The minimal number of instances at the terminal nodes. (default 2)

N: The number of folds used in the pruning. (default 5)

H: Don't use heuristic search for nominal attributes in multi-class problem (default yes).

G: Don't use Gini index for splitting (default yes), if not information is used.

R: Don't use error rate in internal cross-validation (default yes), but root mean squared error.

A: Use the 1 SE rule to make pruning decision. (default no).

C: Percentage of training data size (0-1] (default 1).

Input Ports

: Training data

Output Ports

: Trained classifier

Popular Predecessors

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Popular Successors

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Views

Weka Node View: Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Workflows

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Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) from the below update site following our NodePit Product and Node Installation Guide:

v5.1

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 2.10.2.v202301130909

On NodePit since: 2023-07-15

Last update: 2024-04-24

KNIME versions: From v3.6 to v5.1

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