Zobrazeno 1 - 10
of 229
pro vyhledávání: '"Nair, Vijayan N."'
In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenience to use. However, they are highly complicated, hard to interpret, an
Externí odkaz:
http://arxiv.org/abs/2410.19154
As machine learning models become increasingly prevalent in critical decision-making models and systems in fields like finance, healthcare, etc., ensuring their robustness against adversarial attacks and changes in the input data is paramount, especi
Externí odkaz:
http://arxiv.org/abs/2408.01300
Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) models are directly interpretable as the functional main effects and intera
Externí odkaz:
http://arxiv.org/abs/2309.02426
This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically creating and integrating input from different sources and assembling d
Externí odkaz:
http://arxiv.org/abs/2308.09341
Predictive power and generalizability of models depend on the quality of features selected in the model. Machine learning (ML) models in banks consider a large number of features which are often correlated or dependent. Incorporation of these feature
Externí odkaz:
http://arxiv.org/abs/2307.14327
In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known
Externí odkaz:
http://arxiv.org/abs/2305.15670
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), and Feedforwar
Externí odkaz:
http://arxiv.org/abs/2211.08536
There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrate
Externí odkaz:
http://arxiv.org/abs/2208.06096
Low-order functional ANOVA (fANOVA) models have been rediscovered in the machine learning (ML) community under the guise of inherently interpretable machine learning. Explainable Boosting Machines or EBM (Lou et al. 2013) and GAMI-Net (Yang et al. 20
Externí odkaz:
http://arxiv.org/abs/2207.06950
Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to systematically examine the effects of two sources: randomness in model t
Externí odkaz:
http://arxiv.org/abs/2206.12353