Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm

Table 1 Table showing a comparative analysis of accuracy, precision, recall and F1-Score for three distinct models on simulated dataset: multinomial logistic regression, decision trees and random forest

	Multinomial Logistic Regression	Decision Trees	Random Forest
Accuracy	70.00%	59.05%	78.50%
Precision	0.715	0.600	0.794
Recall	0.700	0.590	0.786
F1-Score	0.701	0.592	0.785

ISSN: 1472-6947