TY - JOUR
AU - 
AB - molecules Article Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics 1 2 , 1 Anita Rácz , Dávid Bajusz * and Károly Héberger Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary * Correspondence: bajusz.david@ttk.mta.hu Received: 17 July 2019; Accepted: 30 July 2019; Published: 1 August 2019 Abstract: Machine learning classification algorithms are widely used for the prediction and classification of the di erent properties of molecules such as toxicity or biological activity. The prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of di erent performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of 
TI - Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics
JF - Molecules
DO - 10.3390/molecules24152811
DA - 2019-08-01
UR - https://www.deepdyve.com/lp/unpaywall/multi-level-comparison-of-machine-learning-classifiers-and-their-7Df64qELOx
DP - DeepDyve
ER -