Machine learning interpretability refers to techniques for explaining and understanding how machine learning models make predictions. As models become more complex, it becomes increasingly important to explain their internal logic and gain insights into their behavior. This is important because machine learni...
这些术语具有不同的含义,但经常互换使用。 在开创性的论文Psychological Foundations of Explainability and Interpretability in Artificial Intelligence中,美国国家标准与技术研究院( NIST )的研究人员提出了以下解释性和可解释性的定义: 可解释性 可解释性是一种低级、详细的心理表征,旨在描述一些复杂的过程。解释描述了...
Machine learning models are becoming increasingly complex, powerful, and able to make accurate predictions. However, as these models become "black boxes," it's even harder to understand how they arrived at those predictions. This has led to a growing focus on machine learning interpretability and...
但是，机器学习模型的难以解释的特性一直为人们所诟病，尤其是预测精度高的模型往往复杂度更高和规模更大，解释性（Interpretability）更差。而对于QSAR模型，其中分子结构与活性的关系比起普通的图像识别任务中标签和图片关系更难以理解，这增加了解释模型的难度。这些复杂的机器学习模型的解释有着重要的意义，可以提高模型...
在开创性的论文Psychological Foundations of Explainability and Interpretability in Artificial Intelligence中,美国国家标准与技术研究院( NIST )的研究人员提出了以下解释性和可解释性的定义: 可解释性 可解释性是一种低级、详细的心理表征,旨在描述一些复杂的过程。解释描述了一些模型机制或输出是如何产生的。
A game theoretic approach to explain the output of any machine learning model. shap.readthedocs.io Topics machine-learningdeep-learninggradient-boostinginterpretabilityshapleyshapexplainability Resources Readme License MIT license Activity Custom properties ...
The accuracy gained by using an ensemble method is offset by the loss in interpretability of the new model. This is the essence of the infamous accuracy-interpretability trade-off. What is LIME? Not long ago, we used to think this trade-off was inevitable. Fortunately, researchers have propos...
SHAPis a Python library that uses Shapley values to explain the output of any machine learning model. To install SHAP, type: pip install shap Train a Model To understand how SHAP works, we will experiment with anadvertising dataset: We will build a machine learning model to predict whether a...
machine-learningdeep-learninggradient-boostinginterpretabilityshapleyshapexplainability UpdatedJan 29, 2025 Jupyter Notebook mljar/mljar-supervised Star3.1k Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation ...
但是,机器学习模型的难以解释的特性一直为人们所诟病,尤其是预测精度高的模型往往复杂度更高和规模更大,解释性(Interpretability)更差。而对于QSAR模型,其中分子结构与活性的关系比起普通的图像识别任务中标签和图片关系更难以理解,这增加了解释模型的难度。这些复杂的机器学习模型的解释有着重要的意义,可以提高模型与...