What Is Unsupervised Learning MCQ?

Are you curious to know what is unsupervised learning MCQ? You have come to the right place as I am going to tell you everything about unsupervised learning MCQ in a very simple explanation. Without further discussion let’s begin to know what is unsupervised learning MCQ?

What Is Unsupervised Learning MCQ?

Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data without any prior knowledge of the outcome or target variable. It is used to identify hidden structures and patterns in the data and group them into clusters or categories. In this blog, we will discuss some multiple-choice questions (MCQs) related to unsupervised learning to test your knowledge.

What Is Unsupervised Learning?

  1. A type of machine learning where the algorithm learns patterns and relationships in data without any prior knowledge of the outcome or target variable.
  2. A type of machine learning where the algorithm is trained on labeled data to predict the target variable.
  3. A type of machine learning where the algorithm is trained on a combination of labeled and unlabeled data to predict the target variable.

Answer: A

Which Of The Following Is An Example Of Unsupervised Learning?

  1. Image classification
  2. Spam filtering
  3. Clustering

Answer: C

What Is Clustering?

  1. A process of grouping similar objects together based on their characteristics.
  2. A process of labeling data to predict the target variable.
  3. A process of identifying outliers in the data.

Answer: A

Which Of The Following Algorithms Is Used For Clustering?

  1. Decision tree
  2. Random forest
  3. K-means

Answer: C

Which Of The Following Evaluation Metrics Is Used For Clustering?

  1. F1 score
  2. Accuracy
  3. Silhouette score

Answer: C

What Is Dimensionality Reduction?

  1. A process of reducing the number of features in the data.
  2. A process of increasing the number of features in the data.
  3. A process of labeling data to predict the target variable.

Answer: A

Which Of The Following Algorithms Is Used For Dimensionality Reduction?

  1. Support vector machine
  2. Principal component analysis
  3. Naive Bayes

Answer: B

Which Of The Following Is An Advantage Of Unsupervised Learning?

  1. It does not require labeled data.
  2. It provides accurate predictions.
  3. It is faster than supervised learning.

Answer: A

Conclusion

In conclusion, unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data without any prior knowledge of the outcome or target variable. It is used to identify hidden structures and patterns in the data and group them into clusters or categories. Clustering is an example of unsupervised learning, and K-means is a commonly used algorithm for clustering. Dimensionality reduction is another application of unsupervised learning, which is used to reduce the number of features in the data. Unsupervised learning is advantageous because it does not require labeled data.

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FAQ

What Is An Unsupervised Machine Learning MCQ?

Unsupervised learning: Unsupervised learning deals with unlabeled data. No training data set is provided which means, no training will be given to the machine.

Which Of The Following Is An Unsupervised Learning MCQ?

Explanation: Pattern classification involves supervised learning while grouping is an unsupervised one.

What Is Unsupervised Learning?

What is unsupervised learning? Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Is It A Type Of Unsupervised Learning MCQ?

Explanation: In unsupervised learning, the model learns itself from the data without having a predicted result. Either the data is not given with a target response variable (label), or none chooses to label a response. In general, it is mainly treated as a pre-processing step for supervised learning models.

 

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