Learn how to use unsupervised learning, a type of machine learning that does not require labels or predefined categories, to organize documents into distinct groups based on their content. Find out how unsupervised learning models can perform clustering, a specific type of unsupervised learning task, and compare them to other types of machine learning models.
Table of Contents
Question
You want to organize documents into distinct groups, without predefining the groups. Which type of machine learning model should you use?
A. Unsupervised learning model
B. Supervised learning model
C. Discriminative deep learning model
D. Natural language processing (NLP) model
Answer
A. Unsupervised learning model
Explanation
The correct answer is A. Unsupervised learning model.
Unsupervised learning is a type of machine learning that does not require any labels or predefined categories for the data. It aims to discover the underlying structure or patterns in the data by grouping similar data points together. Unsupervised learning models can be used for tasks such as clustering, dimensionality reduction, anomaly detection, or association rule mining.
Clustering is a specific type of unsupervised learning that partitions the data into distinct groups or clusters, such that the data points within each cluster are more similar to each other than the data points in different clusters. Clustering can be used to organize documents into meaningful categories based on their content, without knowing the number or the names of the categories beforehand. Clustering algorithms can be based on different criteria, such as distance, density, connectivity, or probability.
The other options are not suitable for organizing documents into distinct groups without predefining the groups, as they are either supervised or not related to clustering:
- Supervised learning model. Supervised learning is a type of machine learning that requires labels or predefined categories for the data. It aims to learn a function that maps the input data to the output labels by using examples of input-output pairs. Supervised learning models can be used for tasks such as classification, regression, or ranking. Classification is a specific type of supervised learning that assigns a label or category to each data point, such as spam or not spam for emails. However, classification requires knowing the number and the names of the categories beforehand, which is not the case for the given task.
- Discriminative deep learning model. Discriminative deep learning is a type of machine learning that uses deep neural networks to learn complex and non-linear functions that can discriminate between different classes or outcomes. Discriminative deep learning models can be used for tasks such as image recognition, natural language processing, or speech recognition. However, discriminative deep learning is usually a form of supervised learning, as it requires labels or predefined categories for the data, which is not the case for the given task.
- Natural language processing (NLP) model. Natural language processing is a branch of artificial intelligence that deals with the analysis and generation of natural language, such as text or speech. Natural language processing models can be used for tasks such as machine translation, sentiment analysis, summarization, or question answering. However, natural language processing is not a type of machine learning, but rather an application domain that can use different types of machine learning models, such as supervised, unsupervised, or semi-supervised learning models.
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