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所在平台: Udemy |
课程主页: https://www.udemy.com/course/formacao-data-science-boosting-com-python/
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《信用评分 - Python 提升模型模块2》课程总结 本课程全面介绍了机器学习中的“提升”（Boosting）技术，旨在将弱学习器转化为强学习器，有效降低模型偏差和方差。课程深入探讨了多种提升算法的实现，包括AdaBoosting、Gradient Boosting和XGBoosting。 通过实际操作，学员将构建一个信用评分模型，并将其性能与逻辑回归模型进行对比分析。课程内容设计循序渐进，首先帮助学员理解各种算法的理论基础，随后进行Python实践。 实践环节将在Google Colab环境中进行，无需额外安装任何软件。课程的核心在于解答“弱学习器能否组合成一个强学习器”这一机器学习中的关键问题，并展示了从弱学习器到高精度学习器的转化过程。**Boosting**算法，尤其是Adaboost（Adaptative Boosting）和Gradient Boosting，是实现这一目标的重要方法。 **核心概念：** * **提升（Boosting）：** 一种集成学习元算法，通过连续训练弱学习器并加权组合，最终形成一个强学习器。 * **弱学习器（Weak Learner）：** 分类性能略好于随机猜测的模型。 * **强学习器（Strong Learner）：** 分类性能任意接近真实分类的模型。 * **AdaBoost (Adaptive Boosting)：** 一种经典的提升算法，通过调整错误样本的权重来迭代训练。 * **Gradient Boosting：** 利用梯度下降思想，以残差为目标进行函数拟合的提升算法。 * **XGBoost：** 一种高效、灵活、可扩展的梯度提升实现，广泛应用于各种机器学习竞赛和实际项目中。 * **信用评分：** 利用机器学习模型预测个人信用风险的应用场景。 **实践重点：** * 在Python中使用Google Colab进行模型开发。 * 构建信用评分模型。 * 对比不同提升算法与逻辑回归模型的性能。 本课程是学习高级机器学习算法（尤其是提升技术）在实际应用中的一个优秀入门。
Neste curso é apresentado a técnica de boosting.São exploradas diferentes tipos de implementações como Adaboosting, Gradiente Boosting e XGBoosting.É realizada a construção de um credit score e a comparação da performance com o modelo de regressão logística.O conteúdo é pensado para ser construído de forma incremental em que o aluno é convidado a pensar sobre os fundamentos dos algoritmos e depois é apresentado para a prática.A prática é realizada em python, com o uso do Google Colab. Você não precisará instalar nada!In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting.When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. "Informally, [the hypothesis boosting] problem asks whether an efficient learning algorithm […] that outputs a hypothesis whose performance is only slightly better than random guessing [i.e. a weak learner] implies the existence of an efficient algorithm that outputs a hypothesis of arbitrary accuracy [i.e. a strong learner]." Algorithms that achieve hypothesis boosting quickly became simply known as "boosting". Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining), as a general technique, is more or less synonymous with boosting.
