Recent research compared the ability of various classification algorithms [logistic regression (LR), random forests (RF), support vector machines (SVM), boosted regression (BR), multi-layer perceptron neural net model (MLP), and classification tree analysis (CTA)] to correctly fail to identify a relationship between a binary class (dependent) variable and ten randomly generated attributes (covariates): only CTA failed to find a model. We use the same ten-variable N=1,000 dataset to assess training classification accuracy of models developed by logistic discriminant analysis (LDA), generalized structural equation modelling (GSEM), and robust diagonally-weighted least-squares (DWLS) SEM for binary outcomes. Except for CTA, all machine-learning algorithms assessed thus far have identified training effects in random data.
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Author Posting © Optimal Data Analysis LLC, 2019. This article is posted here by permission of Optimal Data Analysis LLC for personal use, not for redistribution. The article was published in Optimal Data Analysis, Volume 8, May 2019, https://odajournal.files.wordpress.com/2019/05/v8a21.pdf