Package weka.classifiers.functions

Examples of weka.classifiers.functions.Logistic$OptEng


          }
        }
      }

      // Build logistic regression model
      m_logistic = new Logistic();
      m_logistic.buildClassifier(data);
    }
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          }
        }
      }

      // Build logistic regression model
      m_logistic = new Logistic();
      m_logistic.buildClassifier(data);
    }
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        insample_ROC = new double[2];
        validation_ACC = new double[2];
        validation_ROC = new double[2];
           
        //LOGISTIC REGRESSION  
        Classifier loggy_class = new Logistic();
        Evaluation loggy_eval = new Evaluation(learn);
        loggy_eval.crossValidateModel(Classifier.makeCopy(loggy_class), learn, 5, new Random(42));
            insample_ACC[0] = loggy_eval.pctCorrect();
            insample_ROC[0] = loggy_eval.weightedAreaUnderROC();
       
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        case SMO:
          SMO smo = new SMO();
          smo.setOptions(Utils.splitOptions("-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
          return smo;
        case LOGISTIC:
          Logistic logistic = new Logistic();
          logistic.setOptions(Utils.splitOptions("-R 1.0E-8 -M -1"));
          return logistic;
        default:
          throw new IllegalArgumentException("Classifier " + classifier + " not found!");
      }
    }
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        rbf.setGamma(0.5);
        smo.setKernel(rbf);
        smo.setC(1.5);
       
        //These also work pretty ok
        Logistic log = new Logistic();
        log.setRidge(100);
       
        Classifier classifier = log;

        try {
            System.out.print("Training on " + examples.size() + " examples... ");
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