Examples of weka.classifiers.functions.LinearRegression

weka.classifiers.functions.LinearRegression
Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
Valid options are:
```
 -D Produce debugging output. (default no debugging output)
```
```
 -S <number of selection method> Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
```
```
 -C Do not try to eliminate colinear attributes. 
```
```
 -R <double> Set ridge parameter (default 1.0e-8). 
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5928 $


    reducedInst = Filter.useFilter(reducedInst, attributeFilter);
    
    // build a linear regression for the training data using the
    // tested attributes
    LinearRegression temp = new LinearRegression();
    temp.buildClassifier(reducedInst);


    double [] lmCoeffs = temp.coefficients();
    double [] coeffs = new double [m_instances.numAttributes()];


    for (int i = 0; i < lmCoeffs.length - 1; i++) {
      if (indices[i] != m_classIndex) {
  coeffs[indices[i]] = lmCoeffs[i];

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        insample_ACC = new double[1];
        insample_ROC = new double[1];
        validation_ACC = new double[1];
        validation_ROC = new double[1];
            
        Classifier linReg = new LinearRegression();
        Evaluation eval = new Evaluation(learn);
        eval.crossValidateModel(Classifier.makeCopy(linReg), learn, 5, new Random(42));
            insample_ACC[0] = eval.pctCorrect();
            insample_ROC[0] = eval.weightedAreaUnderROC();

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