Packages

class PGMHD3cp extends BayesClassifier

The PGMHD3cp class implements a three-level Bayes Classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the frequency/population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------

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  1. PGMHD3cp
  2. BayesClassifier
  3. BayesMetrics
  4. ClassifierInt
  5. Classifier
  6. Model
  7. ConfusionFit
  8. Error
  9. QoF
  10. AnyRef
  11. Any
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Instance Constructors

  1. new PGMHD3cp(x: MatriI, nx: Int, y: VectoI, fn_: Strings = null, k: Int, cn_: Strings = null, vc: Array[Int] = null, me: Float = me_default)

    x

    the integer-valued data vectors stored as rows of a matrix

    nx

    the number of x features/columns

    y

    the class vector, where y(l) = class for row l of the matrix x, x(l)

    fn_

    the names for all features/variables

    k

    the number of classes

    cn_

    the names for all classes

    vc

    the value count (number of distinct values) for each feature

    me

    use m-estimates (me == 0 => regular MLE estimates)

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.

    Definition Classes
    ConfusionFit
  5. var additive: Boolean
    Attributes
    protected
    Definition Classes
    BayesClassifier
  6. def aic(vc: Array[Int], vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = me_default): Double

    Compute the 'AIC' for the given Bayesian Network structure and data.

    Compute the 'AIC' for the given Bayesian Network structure and data.

    vc

    the value count

    vcp1

    the value count for parent 1

    vcp2

    the value count for parent 2

    popX

    the population counts

    k

    the number of classes

    me

    the m-estimate value

    Definition Classes
    BayesMetrics
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def buildModel(testStart: Int, testEnd: Int): (Array[Boolean], DAG)

    Build a model.

    Build a model.

    testStart

    starting index of test region (inclusive) used in cross-validation

    testEnd

    ending index of test region (exclusive) used in cross-validation

  9. def calcCMI(idx: VectorI, vca: Array[Int]): MatrixD

    Compute the conditional mutual information matrix.

    Compute the conditional mutual information matrix.

    idx

    indicies of either training or testing region

    vca

    array of value counts

    Definition Classes
    BayesClassifier
  10. def calcCorrelation: MatriD

    Calculate the correlation matrix for the feature vectors 'fea'.

    Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.

    Definition Classes
    ClassifierInt
  11. def calcCorrelation2(zrg: Range, xrg: Range): MatriD

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.

    zrg

    the range of Z-columns

    xrg

    the range of X-columns

    Definition Classes
    ClassifierInt
  12. def classify(u: VectoI): (Int, String, Double)

    Given a discrete data vector 'u', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability.

    Given a discrete data vector 'u', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its relative probability.

    u

    the data vector to classify

    Definition Classes
    PGMHD3cpClassifier
  13. def classify(xx: MatriI = x): VectoI

    Classify all of the row vectors in matrix 'xx'.

    Classify all of the row vectors in matrix 'xx'.

    xx

    the row vectors to classify (defaults to x)

    Definition Classes
    ClassifierInt
  14. def classify(z: VectoD): (Int, String, Double)

    Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.

    Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability. Override in classes that require precise real values for classification.

    z

    the real vector to classify

    Definition Classes
    ClassifierIntClassifier
  15. def clearConfusion(): Unit

    Clear the total cummulative confusion matrix.

    Clear the total cummulative confusion matrix.

    Definition Classes
    ConfusionFit
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  17. def cmiJoint(p_y: VectoD, p_Xy: HMatrix3[Double], p_XyZ: HMatrix5[Double]): MatrixD

    Compute conditional mutual information matrix given the probability of 'y' and joint probabilities of 'Xy' and 'XyZ', where 'y' is the class, and 'X' & 'Z' are features.

    Compute conditional mutual information matrix given the probability of 'y' and joint probabilities of 'Xy' and 'XyZ', where 'y' is the class, and 'X' & 'Z' are features.

    p_y

    the probability of y

    p_Xy

    the joint probability of X and y

    p_XyZ

    the joint probability of X, y and Z

    Definition Classes
    BayesClassifier
    See also

    en.wikipedia.org/wiki/Conditional_mutual_information

  18. var cn: Strings
    Attributes
    protected
    Definition Classes
    ClassifierInt
  19. def computeParent(): Unit

    Compute the parent of each feature based on the correlation matrix.

    Compute the parent of each feature based on the correlation matrix. z features can only select a parent from the x features.

  20. def computeVcp(): Unit

    Compute the value counts of each parent feature based on the parent vector.

  21. 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

    Definition Classes
    ConfusionFit
    See also

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

  22. 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

    Definition Classes
    ConfusionFit
  23. def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method slices out instances/rows to form the test dataset.

    nx

    number of crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    ClassifierIntClassifier
  24. def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.

    nx

    number of crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    ClassifierIntClassifier
  25. 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
    ConfusionFitQoF
    See also

    Regression_WLS

  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  28. def eval(xx: MatriD, yy: VectoD = null): ClassifierInt

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    xx

    the integer-valued test vectors stored as rows of a matrix

    yy

    the classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    ClassifierIntModel
  29. 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

    Definition Classes
    ConfusionFit
  30. def f1v: VectoD

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

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

    Definition Classes
    ConfusionFit
  31. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

    Definition Classes
    QoF
  32. def featureSelection(TOL: Double = 0.01): Unit

    Perform feature selection on the classifier.

    Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.

    TOL

    tolerance indicating negligible accuracy loss when removing features

    Definition Classes
    ClassifierInt
  33. 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
    ConfusionFitQoF
  34. 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
    ConfusionFitQoF
  35. 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.

    Definition Classes
    ConfusionFit
  36. 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
  37. def fitMicroMap: Map[String, VectoD]

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

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

    Definition Classes
    ConfusionFit
  38. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  39. var fn: Strings
    Attributes
    protected
    Definition Classes
    ClassifierInt
  40. val fset: Array[Boolean]

    the set of features to turn on or off.

    the set of features to turn on or off. All features are on by default. Used for feature selection.

    Attributes
    protected
    Definition Classes
    ClassifierInt
  41. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  42. def getParent: Any

    Return the parent (override as needed).

    Return the parent (override as needed).

    Definition Classes
    BayesClassifier
  43. def getY: VectoI

    Return the response (class label) vector.

    Return the response (class label) vector.

    Definition Classes
    ClassifierInt
  44. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  45. 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
    ConfusionFitQoF
  46. def hparameter: HyperParameter

    Return the model hyper-parameters (if none, return null).

    Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

    Definition Classes
    ClassifierIntModel
  47. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  48. 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.

    Definition Classes
    ConfusionFit
    See also

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

  49. def logLikelihood(vc: Array[Int], vcp1: VectoI, vcp2: VectoI, popX: HMatrix5[Int], k: Int, me: Float = me_default): Double

    Compute the Log-Likelihood for the given Bayesian Network structure and data.

    Compute the Log-Likelihood for the given Bayesian Network structure and data.

    vc

    the value count

    vcp1

    the value count for parent 1

    vcp2

    the value count for parent 2

    popX

    the population counts

    k

    the number of classes

    me

    the m-estimate value

    Definition Classes
    BayesMetrics
  50. val m: Int

    the number of data vectors in training/test-set (# rows)

    the number of data vectors in training/test-set (# rows)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  51. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  52. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  53. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  54. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierInt
  55. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierInt
  56. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  57. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  58. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  59. var nu_X: HMatrix2[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  60. var nu_Xy: HMatrix3[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  61. var nu_XyZ: HMatrix5[Int]
    Attributes
    protected
    Definition Classes
    BayesClassifier
  62. val nu_y: VectorI
    Attributes
    protected
    Definition Classes
    BayesClassifier
  63. 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.

    Definition Classes
    ConfusionFit
  64. var p_y: VectorD
    Attributes
    protected
    Definition Classes
    BayesClassifier
  65. def parameter: VectoD

    Return the vector of model parameter values.

    Return the vector of model parameter values.

    Definition Classes
    BayesClassifierModel
  66. def printClassProb(): Unit

    Print the class probabilities.

    Print the class probabilities.

    Definition Classes
    BayesClassifier
  67. 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.

    Definition Classes
    ConfusionFit
  68. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    ClassifierIntModel
  69. def reset(): Unit

    Reset or re-initialize all the frequency hypermatrices to 0.

    Reset or re-initialize all the frequency hypermatrices to 0.

    Definition Classes
    PGMHD3cpClassifier
  70. def setStream(str: Int = 0): Unit

    Set the random number 'stream' to 'str'.

    Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.

    str

    the new fixed random number stream

    Definition Classes
    Classifier
  71. def shiftToZero(): Unit

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Definition Classes
    ClassifierInt
  72. def size: Int

    Return the number of data vectors/points in the entire dataset (training + testing),

    Return the number of data vectors/points in the entire dataset (training + testing),

    Definition Classes
    ClassifierIntClassifier
  73. var smooth: Boolean
    Attributes
    protected
    Definition Classes
    BayesClassifier
  74. val stream: Int

    the random number stream {0, 1, ..., 999} to be used

    the random number stream {0, 1, ..., 999} to be used

    Attributes
    protected
    Definition Classes
    Classifier
  75. 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

    Definition Classes
    ConfusionFit
  76. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  77. def test(xx: MatriI, yy: VectoI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    xx

    the integer-valued test vectors stored as rows of a matrix

    yy

    the test classification vector, where 'yy_i = class' for row 'i' of 'xx'

    Definition Classes
    ClassifierInt
  78. def test(itest: VectorI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    itest

    indices of the instances considered test data

    Definition Classes
    ClassifierIntClassifier
  79. def test(testStart: Int, testEnd: Int): Double

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

    Definition Classes
    Classifier
  80. val tiny: Double
    Attributes
    protected
    Definition Classes
    BayesClassifier
  81. 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)

    Definition Classes
    ConfusionFit
  82. def toString(): String
    Definition Classes
    AnyRef → Any
  83. def toggleSmooth(): Unit

    Toggle the value of the 'smooth' property.

    Toggle the value of the 'smooth' property.

    Definition Classes
    BayesClassifier
  84. def total_cmat(): MatriI

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

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

    Definition Classes
    ConfusionFit
  85. def train(itest: VectorI): PGMHD3cp

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j.

    itest

    the indeices of the test data

    Definition Classes
    PGMHD3cpClassifier
  86. def train(xx: MatriD = null, yy: VectoD = null): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.

    xx

    the data/input matrix (impl. classes should ignore or default xx to x)

    yy

    the response/classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    ClassifierModel
  87. def train(testStart: Int, testEnd: Int): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    starting index of test region (inclusive) used in cross-validation

    testEnd

    ending index of test region (exclusive) used in cross-validation

    Definition Classes
    Classifier
  88. def updateFreq(i: Int): Unit

    Increment/Decrement frequency counters based on the 'i'th row of the data matrix.

    Increment/Decrement frequency counters based on the 'i'th row of the data matrix.

    i

    the index for current data row

    Attributes
    protected
    Definition Classes
    PGMHD3cpBayesClassifier
  89. def vc_default: Array[Int]

    Return default values for binary input data (value count 'vc' set to 2).

    Return default values for binary input data (value count 'vc' set to 2).

    Definition Classes
    ClassifierInt
  90. def vc_fromData: Array[Int]

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    Definition Classes
    ClassifierInt
  91. def vc_fromData2(rg: Range): Array[Int]

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    rg

    the range of columns to be considered

    Definition Classes
    ClassifierInt
  92. val vc_x: Array[Int]
  93. val vc_z: Array[Int]
  94. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  95. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  96. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

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

Inherited from BayesClassifier

Inherited from BayesMetrics

Inherited from ClassifierInt

Inherited from Classifier

Inherited from Model

Inherited from ConfusionFit

Inherited from Error

Inherited from QoF

Inherited from AnyRef

Inherited from Any

Ungrouped