The use case that indicates unsupervised learning from the options provided is Text clustering.

Here's why:

1. Text Clustering: This is an example of unsupervised learning because the algorithm groups the data into different clusters without any prior training. The algorithm itself decides the characteristics that form a cluster based on the structure and pattern of the data.

2. Text Classification: This is a supervised learning task because it requires labeled data to train the model. The model is trained to classify the text into predefined categories.

3. Sentiment Analysis: This is also a supervised learning task. The model is trained with labeled data where each piece of text is associated with a sentiment (like positive, negative, or neutral).

4. News Categorization: This is similar to text classification and is a supervised learning task. The model is trained to categorize news articles into predefined categories like sports, politics, entertainment, etc.

Question

The use case that indicates unsupervised learning from the options provided is Text clustering.

Here's why:

1. Text Clustering: This is an example of unsupervised learning because the algorithm groups the data into different clusters without any prior training. The algorithm itself decides the characteristics that form a cluster based on the structure and pattern of the data.

2. Text Classification: This is a supervised learning task because it requires labeled data to train the model. The model is trained to classify the text into predefined categories.

3. Sentiment Analysis: This is also a supervised learning task. The model is trained with labeled data where each piece of text is associated with a sentiment (like positive, negative, or neutral).

4. News Categorization: This is similar to text classification and is a supervised learning task. The model is trained to categorize news articles into predefined categories like sports, politics, entertainment, etc.

Knowee AI · Accepted Answer

The use case that indicates unsupervised learning from the options provided is Text clustering.

Here's why:

1. Text Clustering: This is an example of unsupervised learning because the algorithm groups the data into different clusters without any prior training. The algorithm itself decides the characteristics that form a cluster based on the structure and pattern of the data.

2. Text Classification: This is a supervised learning task because it requires labeled data to train the model. The model is trained to classify the text into predefined categories.

3. Sentiment Analysis: This is also a supervised learning task. The model is trained with labeled data where each piece of text is associated with a sentiment (like positive, negative, or neutral).

4. News Categorization: This is similar to text classification and is a supervised learning task. The model is trained to categorize news articles into predefined categories like sports, politics, entertainment, etc.

Q.17 Which of the following indicates unsupervised learning use case1. Text clustering2. Text classification3. sentiment analysis4. News Categorization

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