引导式测验 · 中等 等级

集成方法和梯度提升测验

比较 bagging 和 boosting、残差拟合、收缩、正则化、表格数据使用以及树集成的评估工具。

相关引导路径集成方法和梯度提升
问题 1 的 8

What is the core operation in an ensemble method?

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  1. What is the core operation in an ensemble method?
    • Combining predictions from multiple models
    • 使用一个非常大的模型而不是许多小模型
    • 使用尽可能多的数据训练模型
    • 从模型中删除所有弱学习器
  2. How does gradient boosting differ from bagging in the usual training setup?
    • Boosting 并行训练树； bagging 按顺序训练它们
    • Boosting adds learners sequentially; independently fitted bagging learners can be trained in parallel
    • Boosting 仅适用于图像；仅对文本进行装袋
    • 它们在实践中是相同的
  3. In gradient boosting, what target does a new tree approximate?
    • 原始标签
    • 仅特征的随机子集
    • The negative gradient of the current loss, often called a pseudo-residual
    • 之前所有树的平均值
  4. What is a possible effect of using a smaller learning rate in boosting?
    • 增加每棵树的大小
    • 删除表现不佳的树
    • 将树转换为神经网络
    • It can make updates more gradual and may require more trees
  5. What type of input is used in the cited gradient-boosted-tree examples and studies?
    • Structured or tabular data
    • 高分辨率图像
    • 原始音频波形
    • 自然语言长文档
  6. 哪种常见的正则化技术用于防止过度拟合？
    • 删除损失函数
    • 限制树深度和子采样行/特征
    • 始终使用正好 1.0 的学习率
    • 训练无限多的树
  7. What does an out-of-bag score estimate in a bagged tree ensemble?
    • The accuracy on examples used by every tree
    • The number of leaves in the largest tree
    • Generalization performance using training examples omitted from a tree’s bootstrap sample
    • The learning rate of a gradient-boosted model
  8. What does early stopping monitor during gradient-boosting training?
    • Only the training-set row order
    • Whether the model has no trees left
    • The number of categorical columns in a spreadsheet
    • A validation metric and stops adding trees when it no longer improves