Class ClassificationMetrics

java.lang.Object
javax.visrec.ml.eval.EvaluationMetrics
deepnetts.eval.ClassificationMetrics
public final class ClassificationMetrics extends javax.visrec.ml.eval.EvaluationMetrics
Various metrics that tell us how good is a classifier. Calculates various classification metrics which are used for classifier evaluation. For multi class classification enables setting to which specific class values refer to.
See Also:

  • Nested Class Summary

    Nested Classes
    Modifier and Type
    Class
    Description
    static final class 
    ClassificationMetrics.Stats
    Average values of commonly used classification metrics.

  • Field Summary

    Fields inherited from class javax.visrec.ml.eval.EvaluationMetrics

    ACCURACY, F_STAT, F1SCORE, MEAN_ABSOLUTE_ERROR, MEAN_SQUARED_ERROR, PRECISION, R_SQUARED, RECALL, RESIDUAL_SQUARE_SUM, RESIDUAL_STANDARD_ERROR, ROOT_MEAN_SQUARED_ERROR

  • Constructor Summary

    Constructors
    Constructor
    Description
    ClassificationMetrics(int trueNegative, int falsePositive, int falseNegative, int truePositive)
    Constructs a new classification metrics using specified arguments.
    ClassificationMetrics(ConfusionMatrix confMatrix)
    Constructs a new classification metrics from specified confusion matrix.
    ClassificationMetrics(ConfusionMatrix confMatrix, String classLabel, int classIdx)
    Constructs a new classification metrics of a single class for multi class classification.

  • Method Summary

    Modifier and Type
    Method
    Description
    static ClassificationMetrics.Stats
    average(ClassificationMetrics[] results)
     
    static ClassificationMetrics[]
    createFrom(ConfusionMatrix confusionMatrix)
    Creates classification metrics from the given confusion matrix.
    float
    getAccuracy()
    Percent of correct classifications (for both positive and negative classes).
    double
    getBalancedAccuracy()
    Balanced accuracy is a good metric to use when data set is not balanced.
    int
    getClassIdx()
     
    String
    getClassLabel()
    Returns class label that these metric correspond to (used for multi class classification).
    ConfusionMatrix
    getConfusionMatrix()
    Returns a confusion matrix that is used to generate these metrics.
    float
    getErrorRate()
    A percent of wrong classifications/predictions made.
    float
    getF1Score()
    Calculates and returns F1 score - a balance between recall and precision. f1 = 2 * ( (precision*recall) / (precision+recall))
    float
    getFalseDiscoveryRate()
    When its actually no, how often it is classified as yes
    float
    getFalseNegativeRate()
    When its actually yes, how often does it predicts no
    float
    getFalsePositiveRate()
    When it's actually no, how often does it predict yes?
    float
    getFScore(int beta)
    Balance between precision and recall.
    double
    getMatthewsCorrelationCoefficient()
    Calculates and returns the matthews corellation coefficient.
    float
    getPrecision()
    What percent of those predicted as positive are really positive.
    float
    getRecall()
    Ratio between those classified as positive compared to those that are actually positive.
    float
    getSpecificity()
    Specificity or true negative rate.
    int
    getTotal()
    Returns total number of classifications.
    float
    negativeFreqency()
    How often does negative class actually occur in the sample
    float
    positiveFreqency()
    How often does positive class actually occur in the sample
    void
    setClassLabel(String classLabel)
    Sets class label to which this metrics corresponds too
    String
    toString()
     

    Methods inherited from class javax.visrec.ml.eval.EvaluationMetrics

    get, set

    Methods inherited from class Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

  • Constructor Details

    • ClassificationMetrics

      public ClassificationMetrics(ConfusionMatrix confMatrix)
      Constructs a new classification metrics from specified confusion matrix.
      Parameters:
      confMatrix - confusion matrix to extract metrics from.

    • ClassificationMetrics

      public ClassificationMetrics(ConfusionMatrix confMatrix, String classLabel, int classIdx)
      Constructs a new classification metrics of a single class for multi class classification.
      Parameters:
      confMatrix -
      classLabel -
      classIdx -

    • ClassificationMetrics

      public ClassificationMetrics(int trueNegative, int falsePositive, int falseNegative, int truePositive)
      Constructs a new classification metrics using specified arguments.
      Parameters:
      trueNegative -
      falsePositive -
      falseNegative -
      truePositive -

  • Method Details

    • getClassLabel

      public String getClassLabel()
      Returns class label that these metric correspond to (used for multi class classification). In case you have multiple classes each class has its classification metrics.
      Returns:
      class label

    • setClassLabel

      public void setClassLabel(String classLabel)
      Sets class label to which this metrics corresponds too
      Parameters:
      classLabel -

    • getClassIdx

      public int getClassIdx()

    • getConfusionMatrix

      public ConfusionMatrix getConfusionMatrix()
      Returns a confusion matrix that is used to generate these metrics.
      Returns:

    • getAccuracy

      public float getAccuracy()
      Percent of correct classifications (for both positive and negative classes). Answers the question how often a classifier gives correct answer. Accuracy is a good measure classes in the data are nearly balanced. This metric might be misleading if the classes are not balanced. Accuracy = ( TruePositive + TrueNegative ) / Total
      Returns:
      how often is the classifier correct

    • getErrorRate

      public float getErrorRate()
      A percent of wrong classifications/predictions made. Answers the question how often a classifier gives wrong answer? error = (fp + fn) / total error = 1 - accuracy
      Returns:
      classification error rate

    • getPrecision

      public float getPrecision()
      What percent of those predicted as positive are really positive. Answers the question: when it predicts yes, how often is it correct? precision = truePositive / (truePositive + falsePositive)
      Returns:
      percent of those predicted as positive that are really positive.

    • getRecall

      public float getRecall()
      Ratio between those classified as positive compared to those that are actually positive. Also called Sensitivity or True Positive Rate.
      Returns:
      how often classifier predicts yes, when actual class is yes

    • getSpecificity

      public float getSpecificity()
      Specificity or true negative rate. When it's actually no, how often does it predict no?
      Returns:

    • getF1Score

      public float getF1Score()
      Calculates and returns F1 score - a balance between recall and precision. f1 = 2 * ( (precision*recall) / (precision+recall))
      Returns:
      f-score metric (harmonic average of recall and precision)

    • getFScore

      public float getFScore(int beta)
      Balance between precision and recall.
      Parameters:
      beta -
      Returns:
      f-score

    • getTotal

      public int getTotal()
      Returns total number of classifications.
      Returns:
      total number of classifications

    • getFalsePositiveRate

      public float getFalsePositiveRate()
      When it's actually no, how often does it predict yes? FP/actual no
      Returns:

    • positiveFreqency

      public float positiveFreqency()
      How often does positive class actually occur in the sample
      Returns:

    • negativeFreqency

      public float negativeFreqency()
      How often does negative class actually occur in the sample
      Returns:

    • getFalseNegativeRate

      public float getFalseNegativeRate()
      When its actually yes, how often does it predicts no
      Returns:

    • getFalseDiscoveryRate

      public float getFalseDiscoveryRate()
      When its actually no, how often it is classified as yes
      Returns:

    • getMatthewsCorrelationCoefficient

      public double getMatthewsCorrelationCoefficient()
      Calculates and returns the matthews corellation coefficient. The F1 metric is not a suitable method of combining precision and recall i measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The coefficient takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes.[5] The MCC is in essence a correlation coefficient between the observed and predicted binary classifications; it returns a value between −1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and −1 indicates total disagreement between prediction and observation.
      Returns:
      value of matthews correlation coeffiicent

    • getBalancedAccuracy

      public double getBalancedAccuracy()
      Balanced accuracy is a good metric to use when data set is not balanced. It is an average of specificity and recall(sensitivity)
      Returns:

    • toString

      public String toString()
      Overrides:
      toString in class javax.visrec.ml.eval.EvaluationMetrics

    • createFrom

      public static ClassificationMetrics[] createFrom(ConfusionMatrix confusionMatrix)
      Creates classification metrics from the given confusion matrix. Creates an array of ClassificationMetrics objects one for each class.
      Parameters:
      confusionMatrix -
      Returns:
      classification metrics

    • average

      public static ClassificationMetrics.Stats average(ClassificationMetrics[] results)
      Parameters:
      results - list of different metric results computed on different sets of data
      Returns:
      average metrics computed different MetricResults