- 了解机器学习概念
- Python 程式设计经验
观众
- 数据科学家
- 软体工程师
AdaBoost 是一种与其他机器学习模型一起使用以实现最佳性能的演算法。它使用集成学习技术，将较弱的模型组合在一起，以形成更准确的预测。
这种讲师指导的现场培训（在线或现场）面向希望使用 AdaBoost 构建机器学习提升演算法的数据科学家和软体工程师 Python。
在本次培训结束时，参与者将能够：
- 设置必要的开发环境，开始使用 AdaBoost 构建机器学习模型。
- 了解集成学习方法以及如何实现自适应提升。
- 在 Python 中了解如何构建 AdaBoost 模型来提升机器学习演算法。
- 使用超参数优化来提高 AdaBoost 模型的准确性和性能。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在即时实验室环境中动手实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们进行安排。
介绍
- AdaBoost 特性和优势概述
- 了解集成学习方法
开始
- 设置库（Numpy、Pandas、Matplotlib 等）
- 导入或载入数据集
使用 Python 构建 AdaBoost 模型
- 准备用于训练的数据集
- 使用 AdaBoostClassifier 创建实例
- 训练数据模型
- 计算和评估测试数据
使用超参数
- 探索 AdaBoost 中的超参数
- 设置值并训练模型
- 修改超参数以提高性能
最佳实践和故障排除提示
总结和后续步骤
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