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.

中文翻译：

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选择金属纳米粒子的机器学习模型

机器学习的结果受用于描述数据的功能的影响，并且各种指标用于衡量模型的性能。在这项研究中，我们使用五个不同的特征集来描述相同的4000个金纳米颗粒，并使用14个不同的机器学习方法来比较总共70个高得分模型。然后，我们使用分类和回归来表明进行选择时，数据集或机器学习算法的哪些元特征很重要。我们发现，功能的数量以及与功能高度相关的功能，决定了应使用的模型的类别，但是总体质量几乎完全由交叉验证得分决定，而与算法的复杂程度无关。