Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Information Theoretic Methods

The video tutorial discusses various feature selection methods, focusing on information theoretical approaches. It covers information gain, MRMR, and CIFS, highlighting their strengths and limitations, particularly in handling redundancy and their reliance on supervised learning scenarios. The tutorial concludes with a brief mention of upcoming topics on similarity-based methods.

What is the role of redundancy in feature selection methods?

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Discuss the importance of handling feature redundancy in feature selection.

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Summarize the key differences between supervised and unsupervised scenarios in feature selection.

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What challenges arise when applying information theoretical methods to continuous data?

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