A Weighted Solution to SVM Actionability and Interpretability
- URL: http://arxiv.org/abs/2012.03372v1
- Date: Sun, 6 Dec 2020 20:35:25 GMT
- Title: A Weighted Solution to SVM Actionability and Interpretability
- Authors: Samuel Marc Denton and Ansaf Salleb-Aouissi
- Abstract summary: Actionability is as important as interpretability or explainability of machine learning models, an ongoing and important research topic.
This paper finds a solution to the question of actionability on both linear and non-linear SVM models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Research in machine learning has successfully developed algorithms to build
accurate classification models. However, in many real-world applications, such
as healthcare, customer satisfaction, and environment protection, we want to be
able to use the models to decide what actions to take.
We investigate the concept of actionability in the context of Support Vector
Machines. Actionability is as important as interpretability or explainability
of machine learning models, an ongoing and important research topic.
Actionability is the task that gives us ways to act upon machine learning
models and their predictions.
This paper finds a solution to the question of actionability on both linear
and non-linear SVM models. Additionally, we introduce a way to account for
weighted actions that allow for more change in certain features than others. We
propose a gradient descent solution on the linear, RBF, and polynomial kernels,
and we test the effectiveness of our models on both synthetic and real
datasets. We are also able to explore the model's interpretability through the
lens of actionability.
Related papers
- Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
This paper introduces a streamlined method for learning low-dimensional, physics-based models.
We validate our approach through simulations with various planar soft manipulators.
Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
arXiv Detail & Related papers (2024-10-31T18:37:22Z) - SOLD: Reinforcement Learning with Slot Object-Centric Latent Dynamics [16.020835290802548]
Slot-Attention for Object-centric Latent Dynamics is a novel algorithm that learns object-centric dynamics models from pixel inputs.
We demonstrate that the structured latent space not only improves model interpretability but also provides a valuable input space for behavior models to reason over.
Our results show that SOLD outperforms DreamerV3, a state-of-the-art model-based RL algorithm, across a range of benchmark robotic environments.
arXiv Detail & Related papers (2024-10-11T14:03:31Z) - Learning Objective-Specific Active Learning Strategies with Attentive
Neural Processes [72.75421975804132]
Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting.
We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem.
Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
arXiv Detail & Related papers (2023-09-11T14:16:37Z) - Exploring Model Transferability through the Lens of Potential Energy [78.60851825944212]
Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models.
Existing methods for measuring the transferability of pre-trained models rely on statistical correlations between encoded static features and task labels.
We present an insightful physics-inspired approach named PED to address these challenges.
arXiv Detail & Related papers (2023-08-29T07:15:57Z) - Cross Feature Selection to Eliminate Spurious Interactions and Single
Feature Dominance Explainable Boosting Machines [0.0]
Interpretability is essential for legal, ethical, and practical reasons.
High-performance models can suffer from spurious interactions with redundant features and single-feature dominance.
In this paper, we explore novel approaches to address these issues by utilizing alternate Cross-feature selection, ensemble features and model configuration alteration techniques.
arXiv Detail & Related papers (2023-07-17T13:47:41Z) - ComplAI: Theory of A Unified Framework for Multi-factor Assessment of
Black-Box Supervised Machine Learning Models [6.279863832853343]
ComplAI is a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior.
It evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective.
arXiv Detail & Related papers (2022-12-30T08:48:19Z) - MACE: An Efficient Model-Agnostic Framework for Counterfactual
Explanation [132.77005365032468]
We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE)
In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity.
Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
arXiv Detail & Related papers (2022-05-31T04:57:06Z) - Sufficiently Accurate Model Learning for Planning [119.80502738709937]
This paper introduces the constrained Sufficiently Accurate model learning approach.
It provides examples of such problems, and presents a theorem on how close some approximate solutions can be.
The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
arXiv Detail & Related papers (2021-02-11T16:27:31Z) - Model-Based Visual Planning with Self-Supervised Functional Distances [104.83979811803466]
We present a self-supervised method for model-based visual goal reaching.
Our approach learns entirely using offline, unlabeled data.
We find that this approach substantially outperforms both model-free and model-based prior methods.
arXiv Detail & Related papers (2020-12-30T23:59:09Z) - Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy.
We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space.
We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
arXiv Detail & Related papers (2020-07-14T16:42:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.