内容简介· ··· A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase...
that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link betwee...
that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link betwee...
Machine learning (ML) interpretation enables practitioners to understand their models and mitigate risks associated with poor predictions. The first section is a beginner's guide to interpretability, and it starts by recognizing its relevance in business and exploring its key aspects and challenges. It...
The combination of monotonic XGBoost, partial dependence, ICE, and Shapley explanations is likely one of the most direct ways to create an interpretable machine learning model today. Increase Transparency and Accountability in Your Machine Learning Project with Python - Notebook Gradient boosting ...
jphall663 / interpretable_machine_learning_with_python Star 669 Code Issues Pull requests Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. python data-science machine-learning data-mining h2o gradient...
This is the code repository forInterpretable Machine Learning with Python, published by Packt. Learn to build interpretable high-performance models with hands-on real-world examples What is this book about? Do you want to understand your models and mitigate the risks associated with poor predictions...
今天我们介绍可解释机器学习算法的最后一部分,基于XGBoost算法的SHAP值可视化。关于SHAP值其实我们之前的很多个推文中都介绍到,不论是R版本的还是Python版本的,亦不论是普通的分类问题还是生存数据模型的。在此推文中我们将基于XGBoost模型理解SHAP值的计算过程。此外,我们之前的SHAP可视化是基于别人封装好的函数。在今天的...
后续的分享重点将围绕Python与机器学习系列展开。因为在分享过程中发现R中进行机器学习算法运算时还是比较耗时间、耗内存,尤其是在样本量很大的情况下,就不得不借助Python了。另外,Python才是机器学习的主流。后续我们将从Python基础知识逐渐过渡到Python与机器学习系列。欢迎一起学习!
机器学习|SHAP value的另一种R可视化方式以及Python实现SHAP value可视化 机器学习|分享一篇25分临床预测模型文章,再次体现SHAP 值在机器学习中的重要性! R学习|R复现机器学习算法XGBoost特征重要性解释——SHAP value SHAP值在机器学习算法中的重要性主要体现在以下几个方面: ...