Fundamentals of Machine Learning - Going Beyond Linearity

The video tutorial explores the transition from linear to nonlinear models, focusing on polynomial and step functions. It begins with a review of linear regression basics, then introduces polynomial functions as a method to achieve nonlinearity. The tutorial also covers step functions, explaining their role in feature engineering. The importance of model selection and assessment is highlighted, emphasizing trial and error in finding the best model. The tutorial concludes with insights into feature engineering and its potential to enhance model performance.

Describe the role of step functions in feature engineering.

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What are the implications of using indicator functions in regression models?

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Discuss the potential benefits and drawbacks of transforming continuous variables into binary outcomes.

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