Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Regression Exercise S

The video tutorial explores the possibility of treating classification problems as regression problems using a regression API. It explains the data format required for regression, where both feature vectors and targets must be numeric. The tutorial also covers converting classification labels to numeric values for compatibility with regression APIs. However, it highlights the limitations of using classification APIs for regression data due to the need for discrete labels, leading to potential information loss when converting regression targets to discrete labels.

What is the main reason we can treat classification as a regression problem?

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What are the requirements for the feature vectors and targets when using the regression API?

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How can we convert string labels into numeric values for regression?

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Why is it important to keep the regression targets as continuous values rather than converting them to discrete labels?

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What is the limitation of using regression models for classification tasks?

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