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.

Class Summary

Class	Description
AbsoluteDeviation	Absolute deviation error.
Accuracy	The accuracy is the proportion of true results (both true positives and true negatives) in the population.
AdjustedRandIndex	Adjusted Rand Index.
Bootstrap	The bootstrap is a general tool for assessing statistical accuracy.
CrossValidation	Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
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-measure (also F1 score or F-score) considers both the precision p and the recall r of the test to compute the score.
LOOCV	Leave-one-out cross validation.
MSE	Mean squared error.
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.
Validation	A utility class for validating predictive models on test data.

Package smile.validation Description

Model validation.

Author:

Haifeng Li