Examples of org.apache.mahout.classifier.evaluation.Auc.entropy()

Package org.apache.mahout.classifier.evaluation

Class org.apache.mahout.classifier.evaluation.Auc

Examples of org.apache.mahout.classifier.evaluation.Auc.entropy()

org.apache.mahout.classifier.evaluation.Auc.entropy()
Returns a matrix related to the confusion matrix and to the log-likelihood. For a pretty accurate classifier, N + entropy is nearly the same as the confusion matrix because log(1-eps) \approx -eps if eps is small. For lower accuracy classifiers, this measure will give us a better picture of how things work our. Also, by definition, log-likelihood = sum(diag(entropy)) @return Returns a cell by cell break-down of the log-likelihood

      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        System.out.printf("confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
            m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        System.out.printf("entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
            m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }

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      if (showConfusion) {
        output.printf(Locale.ENGLISH, "\n%s\n\n", cm.toString());
        
        if (collector != null){
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }        
      }

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      if (showConfusion) {
        output.printf(Locale.ENGLISH, "%n%s%n%n", cm.toString());


        if (collector != null) {
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]%n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }        
      }

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      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]%n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]%n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }

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      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }

View Full Code Here

      
      if (showConfusion) {
        output.printf(Locale.ENGLISH, "\n%s\n\n", cm.toString());
        
        if (collector != null) {
          Matrix m = collector.entropy();
          output.printf(Locale.ENGLISH,
              "Entropy Matrix: [[%.1f, %.1f], [%.1f, %.1f]]\n", m.get(0, 0),
              m.get(1, 0), m.get(0, 1), m.get(1, 1));
        }        
      }

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      }
      if (showConfusion) {
        Matrix m = collector.confusion();
        output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
        m = collector.entropy();
        output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
          m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
      }
    }
  }

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