These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
2 files

Selecting Machine Learning Models for Metallic Nanoparticles

submitted on 14.05.2020 and posted on 15.05.2020 by Amanda Barnard, George Opletal
The outcome of machine learning is influenced by the features used to describe the data, and various metrics are used to measure model performance. In this study we use five different feature sets to describe the same 4000 gold nanoparticles, and 14 different machine learning methods to compare a total of 70 high scoring models. We then use classification and regression to show which meta-features of data sets or machine learning algorithms are important when making a selection. We find that number of features, and those that are strongly correlated, determine the class of model that should be used, but overall quality is almost entirely determined by the cross-validation score, regardless of the sophistication of the algorithm.


Email Address of Submitting Author


Australian National University



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflicts of interest to declare