BFTree (3.7)

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.


(based on WEKA 3.7)

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

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


BFTree 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).

Select target 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

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Popular Successors

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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.


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