ThresholdSelector (3.7)

A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier

The midpoint threshold is set so that a given performance measure is optimized.Currently this is the F-measure.

Performance is measured either on the training data, a hold-out set or using cross-validation.In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).

(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.


ThresholdSelector Options

C: The class for which threshold is determined. Valid values are: 1, 2 (for first and second classes, respectively), 3 (for whichever class is least frequent), and 4 (for whichever class value is most frequent), and 5 (for the first class named any of "yes","pos(itive)" "1", or method 3 if no matches). (default 5).

X: Number of folds used for cross validation. If just a hold-out set is used, this determines the size of the hold-out set (default 3).

R: Sets whether confidence range correction is applied. This can be used to ensure the confidences range from 0 to 1. Use 0 for no range correction, 1 for correction based on the min/max values seen during threshold selection (default 0).

E: Sets the evaluation mode. Use 0 for evaluation using cross-validation, 1 for evaluation using hold-out set, and 2 for evaluation on the training data (default 1).

M: Measure used for evaluation (default is FMEASURE).

manual: Set a manual threshold to use. This option overrides automatic selection and options pertaining to automatic selection will be ignored. (default -1, i.e. do not use a manual threshold).

S: Random number seed. (default 1)

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

W: Full name of base classifier. (default: weka.classifiers.functions.Logistic)

D: Turn on debugging output.

C: Use conjugate gradient descent rather than BFGS updates.

R: Set the ridge in the log-likelihood.

M: Set the maximum number of iterations (default -1, until convergence).

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, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] 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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