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所在平台: Udemy |
课程主页: https://www.udemy.com/course/feature-importance-and-model-interpretation-in-python/
课程评论：没有评论
课程名称: Python中的特征重要性和模型解释 课程概述: 本课程将重点介绍如何使用Python编程语言在监督机器学习中理解特征重要性和进行模型解释。特征重要性帮助我们更好地理解数据背后的信息，使我们能够在考虑相关信息时减少问题的维数，并排除无用变量。基于特征重要性的常见降维技术是递归特征消除（Recursive Feature Elimination）。模型解释则帮助我们正确分析和理解模型的结果，SHAP技术是计算模型解释的一种常见方法。 在本课程中，您将学习到： - 如何根据模型计算特征重要性。 - 使用SHAP技术计算每个模型的特征重要性。 - 递归特征消除，用于降维，包含交叉验证与不使用交叉验证的两种方式。 每节课都以简要介绍开始，并以Python编程语言及其强大的scikit-learn库中的实际示例结束。本课程将使用Jupyter环境，这是数据科学行业的标准，所有的Jupyter笔记本可供下载。 本课程是我在Coursera平台上开设的“Python中的监督机器学习”在线课程的一部分，其中包含了一些已经在大课程中包含的内容。
In this practical course, we are going to focus on feature importance and model interpretation in supervised machine learning using Python programming language.Feature importance makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common dimensionality reduction technique based on feature importance is the Recursive Feature Elimination.Model interpretation helps us to correctly analyze and interpret the results of a model. A common approach for calculating model interpretation is the SHAP technique.With this course, you are going to learn:How to calculate feature importance according to a modelSHAP technique for calculating feature importance according to every modelRecursive Feature Elimination for dimensionality reduction, with and without the use of cross-validationAll the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the larger course.
