Package smile.validation

Model validation.

See: Description

Interface Summary

Interface	Description
ClassificationMeasure	An abstract interface to measure the classification performance.
ClusterMeasure	An abstract interface to measure the clustering performance.
RegressionMeasure	An abstract interface to measure the regression performance.
Validation	A utility class for validating predictive models on test data.

Class Summary

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.
AdjustedRandIndex	Adjusted Rand Index.
AUC	The area under the curve (AUC).
Bootstrap	The bootstrap is a general tool for assessing statistical accuracy.
ConfusionMatrix	The confusion matrix of truth and predictions.
CrossValidation	Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
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.
FMeasure	The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.
GroupKFold	GroupKfold is a cross validation technique that splits the data by respecting additional information about groups.
LOOCV	Leave-one-out cross validation.
MCC	Matthews correlation coefficient.The MCC is in essence a correlation coefficient between the observed and predicted binary classifications It is considered as a balanced measure for binary classification, even in unbalanced data sets.
MeanAbsoluteDeviation	Mean absolute deviation error.
MSE	Mean squared error.
MutualInformation	Mutual Information for comparing clustering.
NormalizedMutualInformation	Normalized Mutual Information (NMI) for comparing clustering.
Precision	The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.
RandIndex	Rand Index.
Recall	In information retrieval area, sensitivity is called recall.
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.

Enum Summary

Enum	Description
AdjustedMutualInformation.Method	The normalization method.
NormalizedMutualInformation.Method	The normalization method.

Package smile.validation Description

Model validation.