Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Text Features

The video tutorial discusses text features used in information retrieval, focusing on term frequency and term frequency-inverse document frequency (TF-IDF). It explains how these features help in ranking documents based on word frequency and importance. The tutorial includes a practical demonstration using Python and pandas to visualize TF-IDF scores, highlighting their application in text mining and processing.

What are text features used for in information retrieval?

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Explain the process of filtering documents based on specific words.

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What is term frequency and how is it calculated?

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How does the frequency of a term affect its importance in document retrieval?

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Discuss the role of document frequency in calculating TF-IDF.

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Describe the concept of term frequency-inverse document frequency (TF-IDF).

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How can you visualize the feature vector obtained from text data?

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What is the significance of using TF-IDF in text mining?

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What are the limitations of using TF-IDF in all cases?

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What are some common applications of TF-IDF in data science?

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