Table of Contents
Question
A new online camping goods store wants to find connections between products customers buy and other products they might buy. Why would the company use unsupervised learning?
A. Supervised learning is unable to identify connections between unrelated products.
B. Connections can be found with any input required by the user.
C. It does not yet have enough customers to make supervised learning meaningful.
Answer
C. It does not yet have enough customers to make supervised learning meaningful.
Explanation
The correct answer is C. It does not yet have enough customers to make supervised learning meaningful.
Unsupervised learning is a type of machine learning where the goal is to find patterns or structures in unlabeled data without explicit guidance or supervision. In the context of the new online camping goods store, using unsupervised learning can be beneficial for the following reasons:
- Lack of Labeled Data: Supervised learning requires labeled data, meaning that each data point needs to be manually labeled with the desired output or target. In the case of the camping goods store, if the company is new and does not have a substantial customer base yet, it may not have enough labeled data to perform supervised learning effectively. Unsupervised learning allows the company to analyze the existing unlabeled data without relying on labeled examples.
- Exploratory Data Analysis: Unsupervised learning techniques, such as clustering or dimensionality reduction, can help uncover hidden structures, relationships, or groups within the data. By applying unsupervised learning algorithms to the customer purchase data, the camping goods store can gain insights into product associations and identify connections between items that customers are buying together. This information can be valuable for marketing, inventory management, and personalized product recommendations.
- Scalability: Unsupervised learning algorithms can handle large amounts of data without the need for manual labeling. As the online camping goods store grows and collects more customer data, unsupervised learning techniques can scale to analyze and derive insights from the expanding dataset.
Option A, “Supervised learning is unable to identify connections between unrelated products,” is incorrect. Supervised learning is not limited to identifying connections only between related products. It can be used to train models to predict certain outcomes or relationships based on labeled data. However, in this specific scenario, the company may not have enough labeled data to perform meaningful supervised learning.
Option B, “Connections can be found with any input required by the user,” is also incorrect. The choice between supervised and unsupervised learning does not depend on the input required by the user but rather on the availability of labeled data and the specific goals of the analysis. Unsupervised learning is not restricted to any specific type of input but rather focuses on discovering patterns or structures in the data without predefined outputs.
In summary, the new online camping goods store would use unsupervised learning because it may not have enough customers to collect sufficient labeled data for supervised learning. Unsupervised learning allows the company to analyze unlabeled data, uncover connections between products, perform exploratory data analysis, and derive insights without relying on pre-defined labels.
Reference
- What is Unsupervised Learning? | IBM
- Unsupervised learning – Wikipedia
- Unsupervised Learning Definition | DeepAI
- Supervised vs. Unsupervised Learning: What’s the Difference? | IBM
- Unsupervised Machine Learning: Examples and Use Cases | AltexSoft
- Pros and Cons of Unsupervised Learning – Pythonista Planet
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