SimpleLogistic (3.7)

Classifier for building linear logistic regression models

LogitBoost with simple regression functions as base learners is used for fitting the logistic models.The optimal number of LogitBoost iterations to perform is cross-validated, which leads to automatic attribute selection.

For more information see:Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction.

In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.

(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.


SimpleLogistic Options

I: Set fixed number of iterations for LogitBoost

S: Use stopping criterion on training set (instead of cross-validation)

P: Use error on probabilities (rmse) instead of misclassification error for stopping criterion

M: Set maximum number of boosting iterations

H: Set parameter for heuristic for early stopping of LogitBoost. If enabled, the minimum is selected greedily, stopping if the current minimum has not changed for iter iterations. By default, heuristic is enabled with value 50. Set to zero to disable heuristic.

W: Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.

A: The AIC is used to choose the best iteration (instead of CV or training error).

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, Nominal class, Binary 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.

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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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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