Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem. There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships. This paper develops a unified framework using a derivative-based approach for existing tools in the literature, including the partial-dependence plots, marginal plots and accumulated effects plots. It proposes a new interpretation technique called the accumulated total derivative effects plot and demonstrates how its components can be used to develop extensive insights in complex regression models with correlated predictors. The techniques are illustrated through simulation results.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningregressionSimilar Papers 제목 키워드 기반
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific de…
Gaussian ProcessesregressionEquivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals
We study the posterior contraction rates of a Bayesian method with Gaussian process priors in nonparametric regression and its plug-in property for differential operators. For a general class of kernels, we establish con…
Gaussian ProcessesregressionLearning nonparametric differential equations with operator-valued kernels and gradient matching
Modeling dynamical systems with ordinary differential equations implies a mechanistic view of the process underlying the dynamics. However in many cases, this knowledge is not available. To overcome this issue, we introd…
regressionDerivative Estimation in Random Design
We propose a nonparametric derivative estimation method for random design without having to estimate the regression function. The method is based on a variance-reducing linear combination of symmetric difference quotient…
regressionLinear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming, and it is thus computationally appealing…
regression