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. ------------------------------------------------------------------------------
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- new ConfusionFit(y: VectoI, k: Int = 2)
- y
the actual class labels
- k
the number class values
Value Members
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- 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.
- final def asInstanceOf[T0]: T0
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- def clearConfusion(): Unit
Clear the total cummulative confusion matrix.
- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- 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
- 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
- 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
- final def eq(arg0: AnyRef): Boolean
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- 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
- def f1v: VectoD
Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.
- def f_(z: Double): String
Format a double value.
- 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
- 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
- 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.
- def fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinkedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinkedHashMapmakes it ordered).- Definition Classes
- QoF
- def fitMicroMap: Map[String, VectoD]
Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.
- final def flaw(method: String, message: String): Unit
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- def hashCode(): Int
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- def help: String
Return the help string that describes the Quality of Fit (QoF) measures provided by the
ConfusionFitclass.Return the help string that describes the Quality of Fit (QoF) measures provided by the
ConfusionFitclass. Override to correspond to 'fitLabel'.- Definition Classes
- ConfusionFit → QoF
- final def isInstanceOf[T0]: Boolean
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- 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
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- 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.
- 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.
- 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
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
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- 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)
- def toString(): String
- Definition Classes
- AnyRef → Any
- def total_cmat(): MatriI
Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.
- final def wait(arg0: Long, arg1: Int): Unit
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