Package smile.validation.metric

package smile.validation.metric
Model validation metrics. A validation metric provides a quantitative measure of agreement between a predictive model and physical observations.
  • Class
    Description
    Accuracy
    The accuracy is the proportion of true results (both true positives and true negatives) in the population.
    AdjustedMutualInformation
    Adjusted Mutual Information (AMI) for comparing clustering.
    AdjustedMutualInformation.Method
    The normalization method.
    AdjustedRandIndex
    Adjusted Rand Index.
    AUC
    The area under the curve (AUC).
    ClassificationMetric
    An abstract interface to measure the classification performance.
    ClusteringMetric
    An abstract interface to measure the clustering performance.
    ConfusionMatrix
    The confusion matrix of truth and predictions.
    ContingencyTable
    The contingency table.
    CrossEntropy
    Cross entropy generalizes the log loss metric to multiclass problems.
    Error
    The number of errors in the population.
    Fallout
    Fall-out, false alarm rate, or false positive rate (FPR)
    FDR
    The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
    FScore
    The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.
    LogLoss
    Log loss is an evaluation metric for binary classifiers, and it is sometimes the optimization objective as well in case of logistic regression and neural networks.
    MAD
    Mean absolute deviation error.
    MatthewsCorrelation
    Matthews correlation coefficient.
    MSE
    Mean squared error.
    MutualInformation
    Mutual Information for comparing clustering.
    NormalizedMutualInformation
    Normalized Mutual Information (NMI) for comparing clustering.
    NormalizedMutualInformation.Method
    The normalization method.
    Precision
    The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.
    ProbabilisticClassificationMetric
    An abstract interface to measure the probabilistic classification performance.
    R2
    R2.
    RandIndex
    Rand Index.
    Recall
    In information retrieval area, sensitivity is called recall.
    RegressionMetric
    An abstract interface to measure the regression performance.
    RMSE
    Root mean squared error.
    RSS
    Residual sum of squares.
    Sensitivity
    Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test.
    Specificity
    Specificity (SPC) or True Negative Rate is a statistical measures of the performance of a binary classification test.