Packages

class ConfusionFit extends QoF with Error

The ConfusionFit class provides functions for determining the confusion matrix as well as derived Quality of Fit (QoF) measures such as pseudo R-squared, sst, sse, accuracy, precsion, recall, specificity and Cohen's kappa coefficient.

See also

analytics.Fit ------------------------------------------------------------------------------ Must call the 'confusion' method before calling the other methods. ------------------------------------------------------------------------------

Linear Supertypes
Error, QoF, AnyRef, Any
Known Subclasses
BayesClassifier, BayesNetwork, ClassifierInt, ClassifierReal, DecisionTreeC45, DecisionTreeC45wp, DecisionTreeID3, DecisionTreeID3wp, FANBayes, FANBayes0, HiddenMarkov, KNN_Classifier, LDA, LogisticRegression, NaiveBayes, NaiveBayes0, NaiveBayesR, NullModel, OneBAN, OneBAN0, PGMHD3, PGMHD3cp, PGMHD3fl, RandomForest, RandomForest2, SimpleLDA, SimpleLogisticRegression, SupportVectorMachine, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, BayesClassifier, NaiveBayes, NaiveBayes0, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. ConfusionFit
  2. Error
  3. QoF
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new ConfusionFit(y: VectoI, k: Int = 2)

    y

    the actual class labels

    k

    the number class values

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def accuracy: Double

    Compute the accuracy of the classification, i.e., the fraction of correct classifications.

    Compute the accuracy of the classification, i.e., the fraction of correct classifications. Note, the correct classifications 'tp_i' are in the main diagonal of the confusion matrix.

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clearConfusion(): Unit

    Clear the total cummulative confusion matrix.

  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  8. def confusion(yp: VectoI, yy: VectoI = y): MatriI

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is

    yp 0 1 ---------- y 0 | tn fp | 1 | fn tp | ----------

    Note: ScalaTion's confusion matrix is Actual × Predicted, but to swap the position of actual 'y' (rows) with predicted 'yp' (columns) simply use 'cmat.t', the transpose of 'cmat'.

    yp

    the precicted class values/labels

    yy

    the actual class values/labels for full (y) or test (y_e) dataset

    See also

    www.dataschool.io/simple-guide-to-confusion-matrix-terminology

  9. def contrast(yp: VectoI, yy: VectoI = y): Unit

    Contract the actual class 'yy' vector versus the predicted class 'yp' vector.

    Contract the actual class 'yy' vector versus the predicted class 'yp' vector.

    yp

    the predicted class values/labels

    yy

    the actual class values/labels for full (y) or test (y_e) dataset

  10. def diagnose(e: VectoD, yy: VectoD, yp: VectoD, w: VectoD = null, ym: Double = noDouble): Unit

    Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses.

    Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

    e

    the m-dimensional error/residual vector (yy - yp)

    yy

    the actual response vector to use (test/full)

    yp

    the predicted response vector (test/full)

    w

    the weights on the instances (defaults to null)

    ym

    the mean of the actual response vector to use (test/full)

    Definition Classes
    ConfusionFit → QoF
    See also

    Regression_WLS

  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def f1_measure(p: Double, r: Double): Double

    Compute the F1-measure, i.e., the harmonic mean of the precision and recall.

    Compute the F1-measure, i.e., the harmonic mean of the precision and recall.

    p

    the precision

    r

    the recall

  14. def f1v: VectoD

    Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.

  15. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

    Definition Classes
    QoF
  16. def fit: VectoD

    Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.

    Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.

    Definition Classes
    ConfusionFit → QoF
  17. def fitLabel: Seq[String]

    Return the labels for the Quality of Fit (QoF) measures.

    Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

    Definition Classes
    ConfusionFit → QoF
  18. def fitLabel_v: Seq[String]

    Return the labels for the Quality of Fit (QoF) measures.

    Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

  19. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

    Definition Classes
    QoF
  20. def fitMicroMap: Map[String, VectoD]

    Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.

  21. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  23. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  24. def help: String

    Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class.

    Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class. Override to correspond to 'fitLabel'.

    Definition Classes
    ConfusionFit → QoF
  25. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  26. def kappa: Double

    Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

    Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

    See also

    en.wikipedia.org/wiki/Cohen%27s_kappa

  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  30. def p_r_s(): Unit

    Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ...

    Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ... k-1}. -------------------------------------------------------------------------- Precision is the fraction classified as true that are actually true. Recall (sensitivity) is the fraction of the actually true that are classified as true. Specificity is the fraction of the actually false that are classified as false. -------------------------------------------------------------------------- Note, for 'k = 2', ordinary precision 'p', recall 'r' and specificity 's' will correspond to the last elements in the 'pv', 'rv' and 'sv' micro vectors.

  31. def pseudo_rSq: Double

    Compute the Efron's pseudo R-squared value.

    Compute the Efron's pseudo R-squared value. Override to McFadden's, etc.

  32. def summary(b: VectoD = null, show: Boolean = false): String

    Produce a summary report with diagnostics and the overall quality of fit.

    Produce a summary report with diagnostics and the overall quality of fit.

    b

    the parameters of the model

    show

    flag indicating whether to print the summary

  33. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  34. def tn_fp_fn_tp(con: MatriI = cmat): (Double, Double, Double, Double)

    Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).

    Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).

    con

    the confusion matrix (defaults to cmat)

  35. def toString(): String
    Definition Classes
    AnyRef → Any
  36. def total_cmat(): MatriI

    Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.

  37. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  39. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Error

Inherited from QoF

Inherited from AnyRef

Inherited from Any

Ungrouped