Machine learning-based prediction models unleash the enhanced production of fucoxanthin in <i>Isochrysis galbana</i>.

Results The findings revealed that the Random Forest (RF) model was highly significant with a high R 2 of 0.809 and R M S E of 0.776 when hormone descriptors were excluded, and the inclusion of hormone descriptors further improved prediction accuracy to R 2 of 0.839, making it a useful tool for predicting the fucoxanthin yield.

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