difference bet local surrogate model and global surrogate model approximating the behavior of the simulation model - LIME MLmodel Surrogate Unpacking the Difference Between Local Surrogate Models and Global Surrogate Models

Localinterpretablemodelagnostic explanations python In the realm of machine learning and complex simulations, understanding the inner workings of sophisticated "black box" models can be a significant challenge.作者：P Saves·2025·被引用次数：5—From these studies, we identify complementarities and discrepanciesbetween localandglobalexpla- nations and acrosssurrogate models, and we ... These models, while powerful in their predictive capabilities, often lack transparency. This is where the concept of surrogate models becomes invaluable. Surrogate models act as simpler, more interpretable approximations of these complex systems.Asurrogate modelis an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the ... However, not all surrogate models are created equal.A machine learning-based comparative analysis of surrogate ... A crucial distinction lies between two primary approaches: local surrogate models and global surrogate models. Understanding the differences between these two types of surrogate models is key to effectively explaining and optimizing complex systems.6.6 Global Surrogate Models

Global Surrogate Models: A Bird's-Eye View

A global surrogate model aims to approximate the behavior of the entire black-box model across its entire input domainModel interpretability - Azure Machine Learning. Think of it as creating a simplified map of a vast territory; it provides an overarching understanding of the landscape.Are local or global models better? Why not both? The primary goal of a global surrogate model is to offer an overall/global prediction of the black-box model's behavior. This is achieved by training an interpretable model, such as a linear regression or a decision tree, on a dataset that samples the entire input space of the original complex model.

One of the key benefits of a global surrogate model is its ability to provide a comprehensive view. It can reveal general trends, identify key input features that influence the model's output most significantly across all scenarios, and offer a global understanding of the system's response. For instance, in engineering design, a global surrogate model could help designers understand how different material properties, tested across a wide range of values, impact the overall structural integrity of a component. The global surrogate model is trained to be a reasonably accurate approximation for most inputs, providing a unified explanation. This approach is particularly useful when seeking to understand the general relationship between inputs and outputs without needing to delve into the specifics of individual predictions.

Local Surrogate Models: A Zoomed-In Perspective

In contrast to their global counterparts, local surrogate models focus on approximating the behavior of the black-box model within a specific, confined region of the input parameter space.WithLocal Surrogate Models, my understanding is that at alocallevel (for individual predictions). This is computed as: taking alocal... Instead of a broad overview, local surrogate models offer a detailed examination of a particular area of interest. This is akin to zooming in on a specific neighborhood on a map; it provides intricate details about that particular location but less insight into the wider surroundings作者：A Mukhtar·2023·被引用次数：28—This study compares twosurrogatemodelling techniques, polynomial regression (PR) and kriging-basedmodels, and analyses critical issues in design ....

Local surrogate models are particularly powerful for explaining individual predictions. When a user wants to understand *why* a black-box model made a specific prediction for a given set of inputs, a local surrogate model can be trained using data points that are in the immediate vicinity of that specific input.Local surrogate models are interpretable modelsthat are used to explain individual predictions of black box machine learning models. Local interpretable model ... Techniques like LIME model explanation (Local Interpretable Model-Agnostic Explanations) are prime examples of this approach. LIME works by perturbing the input instance and training a simple, interpretable model (like a linear regression) to approximate the behavior of the black box model around that specific instance.What are the differences between global and local ... This localized explanation helps users understand which features were most influential for that particular prediction.2022年2月13日—Global Surrogate Models are used to explain "overall/global predictions" of black-box models while Local Surrogate Models, best represented by ... The Difference between how these models operate is fundamentally about scope: global seeks to explain the whole, while local seeks to explain a part作者：F Charalampakos·2025·被引用次数：3—To evaluate the approximation capability of our approach in alocalexplain- ability setting, we assess whether asurrogate modelcan more..

Key Differences and Applications

The core difference between local surrogate model and global surrogate model lies in their scope of approximation and their primary use cases:

* Scope of Approximation: Global surrogate models approximate the black-box model over its entire input domain, while local surrogate models focus on a specific region or a single data point.

* Explanatory Power: Global surrogate models provide an overall understanding of the model's behavior, useful for identifying general trends and feature importance. Local surrogate models excel at explaining individual predictions, offering insights into the specific factors driving a particular outcomeSurrogate Models: A Comfortable Middle Ground?.

* Computational Cost: While both are generally less computationally expensive than running the original black-box model repeatedly, training a global surrogate model accurately across a large domain can still be demanding.Surrogate modelsmay cover the entire domain and can be used as full replacements of the underlying system. Such surrogates are calledglobal surrogate models. Contrarily,local surrogate modelsonly cover a region of the entire domain of underlying systems with the aim of ... Local surrogate models, by focusing on a smaller region, can sometimes be more computationally efficient for explaining specific instances.

* Accuracy: Global surrogate models might sacrifice accuracy in specific regions to achieve a better overall fit.Global vs. Local Models for Cross-project Defect Prediction Local surrogate models can achieve higher accuracy within their localized scope but offer no guarantees outside of that region.

The choice between a global surrogate model and a local surrogate model often depends on the specific problem and the desired outcome. For instance, if the goal is to optimize a process by understanding how various parameters interact across all possible scenarios, a global surrogate model would be more suitableSurrogate models, which have become a popular approach to oil‐reservoir production‐optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm calledglobal.... However, if the objective is to debug a specific problematic prediction or to provide a clear explanation to an end-user about a particular outcome, a local surrogate model is often the better choiceModel interpretability - Azure Machine Learning.

Combining Approaches for Enhanced Understanding

Recent research suggests that a hybrid approach, combining both global and local surrogate models, can yield superior results. The idea is that global models that are fine-tuned on local data perform better than either approach alone. A global model can assist GP (Gaussian Process) in exploring the entire search space, while a local surrogate model can speed up convergence and further refine the understanding in critical areas. This synergistic approach leverages the broad explanatory power of the global surrogate model with the granular detail offered by local surrogate models, providing a more robust and comprehensive understanding of complex systems. In essence, using both global and local surrogate models allows for both broad exploration and deep dives, offering a more complete picture.

In conclusion, understanding the fundamental difference between local and global surrogate modeling is crucial for any practitioner working with complex, opaque models. Whether seeking a comprehensive overview or a detailed explanation of specific predictions, the strategic application of surrogate models is key to unlocking transparency and driving informed decision-making.