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KNIME GmbH random forest (rf) classifier
The sequence minimization optimization <t>(SMO)</t> and Random Forest (RF) classifier models showed greater predictive accuracy than <t>the</t> <t>Naïve</t> Bayesian (NB) and Voted Perceptron (VP) classifier models.
Random Forest (Rf) Classifier, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/random+forest+algorithm+model/pmc06161899-50-24-4?v=KNIME+GmbH
Average 90 stars, based on 1 article reviews
random forest (rf) classifier - by Bioz Stars, 2026-08
90/100 stars

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Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

Journal: PLoS ONE

doi: 10.1371/journal.pone.0204644

The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.
Figure Legend Snippet: The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.

Techniques Used: Sequencing

Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.
Figure Legend Snippet: Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.

Techniques Used: Sequencing, Plasmid Preparation



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