K 最近邻测验

当您参加测验时，就会出现答案和解释。

1. KNN 如何对新数据点进行分类？
   • 通过获得 K 个最近邻居的多数票
   • 通过在其上训练神经网络
   • 通过计算加权回归线
   • 通过分配随机类别
2. 为什么 KNN 通常被称为“惰性学习器”？
   • It never stores training data
   • It stores training examples and can defer much model-specific work until prediction, though indexes may be built during fitting
   • It always trains a deep neural network first
   • It does no work during either fitting or prediction
3. 为什么特征缩放对于 KNN 很重要？
   • 它使训练更快
   • 它减少了所需邻居的数量
   • 因此，具有大范围的特征不会主导距离
   • 自动改变K
4. 如果您选择一个非常小的 K，例如 K=1，会发生什么？
   • 模型变得非常平滑
   • 模型忽略数据
   • 预测变得不可能
   • 模型对噪声变得敏感并且可能过度拟合
5. 在暴力 KNN 实现中，为什么在大型数据集上预测会很慢？
   • It may compute distances from a query to many or all stored examples
   • It must update all model weights for every query
   • It reruns the entire training dataset through a neural network
   • It cannot use an index or approximate search
6. 哪种现代技术依赖于最近邻思想？
   • 使用 JPEG 进行图像压缩
   • 矢量搜索和检索增强生成
   • 随机数生成
   • 电子表格公式
7. Why should a feature scaler be fit only on the training split before KNN evaluation?
   • To make the distance metric ignore every feature
   • To guarantee all classes have equal counts
   • To avoid using information from validation or test examples when setting the scale
   • To remove the need to choose K
8. How can KNN make a prediction for a regression task?
   • Choose the most common class label only
   • Fit a new deep network for each query
   • Ignore the stored examples and use a fixed intercept
   • Aggregate the target values of the K nearest training examples, often by averaging