Document Type

Article

Publication Date

5-16-2019

Publication Title

Optimal Data Analysis

Volume

8

Pages

97-102

Publisher Name

Optimal Data Analysis LLC

Abstract

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.

Identifier

2155-0182

Comments

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

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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