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所在平台: Udemy |
课程主页: https://www.udemy.com/course/imbalanced-classification-master-class-in-python/
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Python不平衡分类主班 本课程旨在帮助您应对不平衡分类任务，这是一类在样本类别分布不均的预测建模问题。课程将引导您深入理解不平衡分类的挑战与直观认识，并教授您一系列专业技术，以有效处理数据预处理、模型选择、性能评估以及算法应用等方面的问题。 您将学习如何： * 选择适合不平衡分类任务的性能指标。 * 在划分训练集和测试集以及进行K折交叉验证时，正确地分层抽样不平衡数据集。 * 使用SMOTE等数据采样算法转换训练集，并将其应用于标准机器学习模型。 * 利用成本敏感学习算法处理不平衡分类问题。 * 运用改进版的标准算法（如SVM和决策树），考虑类别权重。 * 调整阈值以解释预测概率并分配类标签。 * 校准非线性算法的概率预测。 * 将异常检测和离群点检测算法应用于不平衡分类。 * 使用为考虑类别分布而修改的集成学习算法。 * 系统地完成不平衡分类预测建模项目。 本课程鼓励您通过实践来学习，建议您在学习过程中同步运行和修改Python示例代码，以加深理解和掌握。
Welcome to Imbalanced Classification Master Class in Python.Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification is those classification tasks where the distribution of examples across the classes is not equal. Typically the class distribution is severely skewed so that for each example in the minority class, there may be one hundred or even one thousand examples in the majority class. Practical imbalanced classification requires the use of a suite of specialized techniques, data preparation techniques, learning algorithms, and performance metrics. Let's discuss what you'll learn in this course. The challenge and intuitions for imbalanced classification datasets. How to choose an appropriate performance metric for evaluating models for imbalanced classification. How to appropriately stratify an imbalanced dataset when splitting into train and test sets and when using k-fold cross-validation.How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of standard machine learning models. How algorithms from the field of cost-sensitive learning can be used for imbalanced classification. How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account. How to tune the threshold when interpreting predicted probabilities as class labels. How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework. How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification. How to use modified ensemble algorithms that have been modified to take the class distribution into account during training. How to systematically work through an imbalanced classification predictive modeling project.This course was created to be completed linearly, from start to finish. That being said, if you know the basics and need help with a specific method or type of problem, then you can flip straight to that section and get started. This course was designed for you to completed on your laptop or desktop, on the screen, not on a tablet. My hope is that you have the course open right next to your editor and run the examples as you read about them. This course is not intended to be completed passively or be placed in a folder as a reference text. It is a playbook, a workbook, and a guidebook intended for you to learn by doing and then apply your new understanding with working Python examples. To get the most out of the course, I would recommend playing with the examples in each tutorial. Extend them, break them, then fix them.Thanks for you interest in Imbalanced Classification Master Class in Python. Now let's get started!
