Exam AWS Certified Machine Learning - Specialty topic 1 question 174 discussion

Answer is D! K-means used for customer segmentation

The answer is D. 1. "The current segmentation of consumers is unclear." so it is unsupervised learning. 2. Then K-means is for unsupervised learning.

Typical clustering problem - use K means

KNN is used to solve missing data in regression/supervised problems. Since the question says unknown segmentation, it is an unsupervised problem and K-Means is the right choice. So Option D it is.

D is the correct C is wrong because kNN stills a supervised algorithm

The XGBoost model is a supervised machine learning algorithm, which means it requires labeled data to learn from. However, the customers’ current segmentation is unknown, so there are no labels to train or evaluate the model. The data scientist needs an unsupervised machine learning algorithm, which can discover patterns and clusters in unlabeled data. A k-means model is an example of an unsupervised machine learning algorithm that can partition the data into K groups based on similarity. By setting K = 5, the data scientist can obtain five customer segments based on age, income, and location.

KNN has no k parameter in its input. C is not the answer.

D The key work is that the classification is "unclear", therefore k-means