We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the underlying machine learning model. The metamodel can be estimated from data generated via a trained complex model by running the computer experiment on samples of data in the region of interest. We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model. The optimization procedure for the variable importance parameter theta is to maximize the likelihood function. This theta corresponds to the correlation of individual variables with the target response. There is no need for any pre-assumed models since it depends on empirical observations. Experiments demonstrate the potential of the interpretable model through quantitative assessment of the predicted samples.
翻译:我们提出一个新的框架,用于2D解释(地物和样本)黑盒机器学习模型,通过一种元模型技术,我们研究基础机器学习模型的输出和输入关系。元模型可以通过一个经过训练的复杂模型产生的数据来估计,方法是对相关区域的数据样本进行计算机实验。我们利用高西亚进程作为替代,以捕捉一个复杂模型的反应面,在这个过程中我们包含两个部分:由一个固定的Gaussian进程Z模型的内推值,由先前的常态函数管理,以及一种平均函数,以捕捉基本模型中已知的趋势。变量重要性参数Theta的优化程序是为了最大限度地发挥可能性功能。这个模型与个别变量与目标响应的相互关系相对应。不需要任何预先假设模型,因为它取决于经验观测。实验通过对预测样本进行定量评估来证明可解释模型的潜力。