引导式测验 · 简单 等级

决策树和随机森林测验

检查树决策路径、过度拟合、集成聚合、引导样本、分割标准、提升、随机特征和袋外评估。

相关引导路径决策树和随机森林
问题 1 的 8

决策树如何做出预测？

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  1. 决策树如何做出预测？
    • 通过平均图像中的所有像素
    • 通过在语言之间翻译文本
    • 每次随机猜测
    • 通过一系列是/否问题一直到叶子
  2. 单一深度决策树的主要弱点是什么？
    • 它无法处理数字
    • 它往往会过度拟合并记住训练数据中的噪音
    • 只能输出文本
    • 它需要 GPU 才能运行
  3. How does a random forest combine the predictions of its constituent trees?
    • By aggregating predictions, such as class votes or probabilities
    • By using exactly one tree and discarding every other output
    • By converting all trees into one fixed training row
    • By guaranteeing that all individual errors cancel
  4. What sampling operation does bagging use when constructing conventional bootstrap training sets?
    • Sorting rows only by filename
    • Permanently deleting all numerical features
    • Drawing training rows with replacement
    • Assigning exactly one unique row to every tree
  5. 分类树通常使用什么指标来选择分割？
    • 像素亮度
    • 基尼杂质或熵
    • 音频
    • 字数统计
  6. Which named system is described in the paper A Scalable Tree Boosting System?
    • Photoshop
    • FFmpeg
    • 库伯内斯
    • XGBoost
  7. Which random-forest mechanism varies candidate input features at a split rather than resampling training rows?
    • Bootstrap row sampling alone
    • Alphabetical label sorting
    • Random feature-subset selection
    • A guarantee of identical splits in every tree
  8. For a training row in a bootstrap forest, which trees should contribute to its out-of-bag prediction?
    • Trees whose bootstrap samples omitted that row
    • Only trees whose samples included that row at least twice
    • Every tree regardless of whether it trained on the row
    • A required newly trained tree using only that row