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**KNIME WEKA nodes (3.7)** version **4.1.0.v201911281246** by **KNIME AG, Zurich, Switzerland**

Class for building pace regression linear models and using them for prediction

Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity.It consists of a group of estimators that are either overall optimal or optimal under certain conditions.

The current work of the pace regression theory, and therefore also this implementation, do not handle:

- missing values

- non-binary nominal attributes- the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20)

For more information see:

Wang, Y (2000).

A new approach to fitting linear models in high dimensional spaces.Hamilton, New Zealand.

Wang, Y., Witten, I.

H.: Modeling for optimal probability prediction.In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

(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.

- PaceRegression Options
D: Produce debugging output. (default no debugging output)

E: The estimator can be one of the following: eb -- Empirical Bayes estimator for noraml mixture (default) nested -- Optimal nested model selector for normal mixture subset -- Optimal subset selector for normal mixture pace2 -- PACE2 for Chi-square mixture pace4 -- PACE4 for Chi-square mixture pace6 -- PACE6 for Chi-square mixture ols -- Ordinary least squares estimator aic -- AIC estimator bic -- BIC estimator ric -- RIC estimator olsc -- Ordinary least squares subset selector with a threshold

S: Threshold value for the OLSC estimator

- 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: [Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, 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.

- 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.

- 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.

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:

KNIME 4.1

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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