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• W2019969379 abstract "Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while neural networks behave very similarly to linear discriminant analysis and logistic regression." @default.
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• W2019969379 date "2007-01-01" @default.
• W2019969379 modified "2023-10-03" @default.
• W2019969379 title "Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees" @default.
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• W2019969379 doi "https://doi.org/10.1027/1614-2241.3.2.47" @default.
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