Confusion matrices depict the performance of various ML algorithms.

<p>The elements in the figure labeled A to F correspond to LSTM, CNN, LR, SVM, MLP, and NB, respectively. The diagonal elements represent TP values, showcasing each algorithm’s accuracy in correctly identifying positive instances. This is noteworthy on a test sample containing 180 instances.&l...

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Bibliographic Details
Main Author:	Caroline L. Alves (14271413) (author)
Other Authors:	Tiago Martinelli (10402035) (author), Loriz Francisco Sallum (19865127) (author), Francisco Aparecido Rodrigues (19865130) (author), Thaise G. L. de O. Toutain (19865133) (author), Joel Augusto Moura Porto (19865136) (author), Christiane Thielemann (14271419) (author), Patrícia Maria de Carvalho Aguiar (19865139) (author), Michael Moeckel (19865142) (author)
Published:	2024
Subjects:	Medicine Cell Biology Neuroscience Biotechnology Science Policy Mental Health Biological Sciences not elsewhere classified totaling 120 subjects targeted intervention difficult surpassing existing benchmarks observed connectivity patterns brain network integration autism spectrum disorder achieve superior accuracy brain regions critical leveraging multiclass classification asd show disruptions ml classification rests multiclass classification regions involved typically developed three groups segregation among promising avenue overlapping symptoms impulse control findings pave established practices enhanced diagnostics cognitive functions clinical symptoms accurate diagnosis
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Confusion matrices depict the performance of various ML algorithms.

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