KNIME WEKA nodes (3.7) version 4.4.0.v202104271009 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.
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
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
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:
A zipped version of the software site can be downloaded here.
You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.