KNIME WEKA nodes (3.7) version 4.3.1.v202101261634 by KNIME AG, Zurich, Switzerland
A wrapper class for the liblinear classifier. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008)
LIBLINEAR - A Library for Large Linear Classification.URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.
(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.
S: Set type of solver (default: 1) for multi-class classification 0 -- L2-regularized logistic regression (primal) 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression 7 -- L2-regularized logistic regression (dual) for regression 11 -- L2-regularized L2-loss support vector regression (primal) 12 -- L2-regularized L2-loss support vector regression (dual) 13 -- L2-regularized L1-loss support vector regression (dual)
C: Set the cost parameter C (default: 1)
Z: Turn on normalization of input data (default: off)
N: Turn on nominal to binary conversion.
M: Turn off missing value replacement. WARNING: use only if your data has no missing values.
P: Use probability estimation (default: off) currently for L2-regularized logistic regression, L1-regularized logistic regression or L2-regularized logistic regression (dual)!
E: Set tolerance of termination criterion (default: 0.01)
W: Set the parameters C of class i to weight[i]*C (default: 1)
B: Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)
D: If set, classifier is run in debug mode and may output additional info to the console
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, Nominal class, Binary 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.