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REPTree (3.7)

KNIME WEKA nodes (3.7) version 3.6.0.v201805031010 by KNIME AG, Zurich, Switzerland

Fast decision tree learner

Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).Only sorts values for numeric attributes once.

Missing values are dealt with by splitting the corresponding instances into pieces (i.e.as in C4.5).

(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.

Options

REPTree Options

M: Set minimum number of instances per leaf (default 2).

V: Set minimum numeric class variance proportion of train variance for split (default 1e-3).

N: Number of folds for reduced error pruning (default 3).

S: Seed for random data shuffling (default 1).

P: No pruning.

L: Maximum tree depth (default -1, no maximum)

I: Initial class value count (default 0)

R: Spread initial count over all class values (i.e. don't use 1 per value)

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, Date attributes, Missing values, Nominal class, Binary class, Numeric class, Date class, Missing class values] 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

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.

Update Site

To use this node in KNIME, install KNIME WEKA nodes (3.7) from the following update site:

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