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.

ClassDescriptionThe accuracy is the proportion of true results (both true positives and true negatives) in the population.Adjusted Mutual Information (AMI) for comparing clustering.The normalization method.Adjusted Rand Index.The area under the curve (AUC).An abstract interface to measure the classification performance.An abstract interface to measure the clustering performance.The confusion matrix of truth and predictions.The contingency table.Cross entropy generalizes the log loss metric to multiclass problems.The number of errors in the population.Fallout, false alarm rate, or false positive rate (FPR)The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1  precision.The Fscore (or Fmeasure) considers both the precision and the recall of the test to compute the score.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.Mean absolute deviation error.Matthews correlation coefficient.Mean squared error.Mutual Information for comparing clustering.Normalized Mutual Information (NMI) for comparing clustering.The normalization method.The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.An abstract interface to measure the probabilistic classification performance.R^{2}.Rand Index.In information retrieval area, sensitivity is called recall.An abstract interface to measure the regression performance.Root mean squared error.Residual sum of squares.Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test.Specificity (SPC) or True Negative Rate is a statistical measures of the performance of a binary classification test.