Assessing Predictive Accuracy in Discriminant Analysis
- 1 July 1987
- journal article
- Published by Taylor & Francis in Multivariate Behavioral Research
- Vol. 22 (3) , 307-329
- https://doi.org/10.1207/s15327906mbr2203_4
Abstract
The estimation of probabilities of correct classification is a primary concern in predictive discriminant analysis. Three such probabilities are: (a) the optimal hit rate, that obtained when the classification rule is based on known parameters; (b) the actual hit rate, that obtained by applying a rule based on a particular sample to future samples; and (c) the expected actual hit rate. Methods of estimating these hit rates include formulas (in the two-group case), resubstitution, and external analyses. The methods are tentatively compared via Monte Carlo sampling from two real data sets.Keywords
This publication has 23 references indexed in Scilit:
- Estimation of Allocation Rates in a Cluster Analysis ContextJournal of the American Statistical Association, 1985
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-ValidationJournal of the American Statistical Association, 1983
- Estimation in Multiple Correlation/PredictionEducational and Psychological Measurement, 1980
- Estimation of the predictive power of a regression model.Journal of Applied Psychology, 1980
- Additive estimators for probabilities of correct classificationPattern Recognition, 1978
- The Bias of Sample Based Posterior ProbabilitiesBiometrical Journal, 1977
- Discriminant AnalysisReview of Educational Research, 1975
- Some Expected Values for Probabilities of Correct Classification in Discriminant AnalysisTechnometrics, 1971
- Estimation of Error Rates in Discriminant AnalysisTechnometrics, 1968