RegressionByDiscretization (3.7)

A regression scheme that employs any classifier on a copy of the data that has the class attribute discretized

The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).This class now also supports conditional density estimation by building a univariate density estimator from the target values in the training data, weighted by the class probabilities.

For more information on this process, see

Eibe Frank, Remco R.Bouckaert: Conditional Density Estimation with Class Probability Estimators.

In: First Asian Conference on Machine Learning, Berlin, 65-81, 2009.

(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.


RegressionByDiscretization Options

B: Number of bins for equal-width discretization (default 10).

E: Whether to delete empty bins after discretization (default false).

A: Whether to minimize absolute error, rather than squared error. (default false).

F: Use equal-frequency instead of equal-width discretization.

K: What type of density estimator to use: 0=histogram/1=kernel/2=normal (default: 0).

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.trees.J48)

U: Use unpruned tree.

O: Do not collapse tree.

C: Set confidence threshold for pruning. (default 0.25)

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

R: Use reduced error pruning.

N: Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

B: Use binary splits only.

S: Don't perform subtree raising.

L: Do not clean up after the tree has been built.

A: Laplace smoothing for predicted probabilities.

J: Do not use MDL correction for info gain on numeric attributes.

Q: Seed for random data shuffling (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, Date attributes, Missing values, Numeric class, Date 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: 2

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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