- 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
Random Forest 是一种机器学习演算法，主要用于分类和回归。它利用多个决策树来生成更精确和准确的预测。
这种讲师指导的现场培训（在线或现场）面向希望使用 Random Forest 为大型数据集构建机器学习演算法的数据科学家和软体工程师。
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 Random forest 构建机器学习模型。
- 了解 Random Forest 的优点以及如何实施它来解决分类和回归问题。
- 在 Random Forest 中了解如何处理大型数据集和解释多个决策树。
- 通过优化超参数来评估和优化机器学习模型的性能。
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
- Random Forest 特性和优势概述
- 了解决策树和集成方法
开始
- 设置库（Numpy、Pandas、Matplotlib 等）
- Random Forests 中的分类和回归
- 使用案例和示例
实施 Random Forest
- 准备用于训练的数据集
- 训练机器学习模型
- 评估和提高准确性
调整 Random Forest 中的超参数
- 执行交叉验证
- 随机搜索和网格搜索
- 可视化训练模型性能
- 优化超参数
最佳实践和故障排除提示
总结和后续步骤
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