Which statement is TRUE?

Question

BlackTom AI · Accepted Answer

Let's evaluate each statement in turn, keeping in mind what PCA and K-Means do and how they are typically used in the workflow.

Option 1: 'K-Means clustering reduces features to principal components, whereas PCA projects data onto new axes that capture maximum variance.' This is a mix-up of concepts. K-Means is a clustering algorithm that partitions data into k groups based on distance to centroids; it does not directly reduce features or project data onto principal components. PCA, on the other hand, is a dimensionality reduction technique that projects data onto new axes (principal components) that capture the maximum variance. So this statement incorrectly assigns PCA’s projection role to K-Means and misstates K-Means’ purpose.

Option 2: 'Both PCA and K-Means clustering are unsupervised learning methods that can be used before using supervised learning methods.' This statement correctly characterizes both techniques as unsupervised. It also reflects a common practical workflow where dimensionality reduction (PCA) or clustering (K-Means) is performed as a preprocessing step prior to supervised learning, for example to reduce dimensionality, denoise data, or produce features.

Option 3: 'PCA is typically applied after K‑Means clustering to improve centroid stability.' In practice, PCA is usually applied before clustering (including K-Means) to reduce dimensionality and preserve variance, which can improve clustering performance and stability. Applying PCA after clustering would not affect the already computed centroids and is not the standard approach.

Option 4: 'PCA requires specifying the number of clusters, whereas K-Means clustering determines the number of components automatically.' This statement confuses the two methods. PCA does not involve clusters or a cluster count; it requires choosing how many principal components to retain. K-Means requires selecting the number of clusters k. The roles are opposite from what this option suggests.

Overall, the second option aligns with the actual nature of both methods and their common usage in a preprocessing stage before supervised learning, while the other options contain misconceptions about what each method does and how they are usually applied.

Which statement is TRUE?

查看解析

登录即可查看完整答案

类似问题

如果你想使用聚类来训练一个网上商店的推荐系统，你应该选择哪种学习方法？ If you want to train a recommending system for an online shop using clustering, which learning method should you choose?

无监督学习最适合哪种任务？Which task would unsupervised learning be best suited for?

Of the following examples, which would you address using an "unsupervised" learning algorithm?

Which of the following are examples of unsupervised learning: A. Regression B. Clustering C. Classification

Clustering is a supervised learning

Which method is best for detecting patterns in unlabeled data?

Clustering is a supervised learning

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单