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所在平台: Udemy |
课程主页: https://www.udemy.com/course/imbalanced-learning-the-complete-guide/
课程评论：没有评论
## 课程总结：不平衡学习（非均衡数据）- 完全指南 本课程是针对数据科学和机器学习领域学生的入门课程，深入探讨数据挖掘和机器学习中的核心挑战——**不平衡学习**。随着数据量的爆炸式增长，从海量信息中发现知识变得尤为重要，然而，现实世界中的许多数据集都存在类别分布不均，即**不平衡数据**的问题。 **不平衡数据**的核心挑战在于，当训练数据中某些类别的样本远少于其他类别时，学习算法倾向于偏重多数类，而忽略少数类，导致模型在预测少数类时性能低下。 本课程旨在帮助学员： * **理解不平衡数据问题的根本原因**：剖析导致数据不平衡的具体因素。 * **掌握处理不平衡数据的先进方法和技术**：介绍当前主流的、状态最先进的解决方案，包括数据层面的处理（如重采样）和算法层面的改进。 * **辨析不同方法的优缺点**：深入分析各种应对不平衡学习的技术的优势与局限性，帮助学员做出明智的选择。 * **学习评估不平衡学习的有效指标**：重点讲解用于正确评估不平衡学习解决方案效果的关键评估指标，确保学员能够准确衡量模型性能。 通过本课程的学习，学员将全面掌握识别、理解和有效解决不平衡学习问题所需的知识和技能。
This is a niche topic for students interested in data science and machine learning fields. The classical data imbalance problem is recognized as one of the major problems in the field of data mining and machine learning. Imbalanced learning focuses on how an intelligent system can learn when it is provided with unbalanced data.There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from unbalanced data poses major challenges and is recognized as needing significant attention. The problem with unbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions. The specific goals of this course are: Help the students understand the underline causes of unbalanced data problem. Go over the major state-of-the-art methods and techniques that you can use to deal with imbalanced learning. Explain the advantages and drawback of different approaches and methods.Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.
