Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Derivations for Math Lovers (Optional): Lo

The video tutorial explains logistic regression using maximum likelihood estimation on Bernoulli random variables. It begins with an introduction to Bernoulli variables and binary classification, followed by a transformation of feature vectors. The tutorial then models the probability of success using a sigmoid function, which is central to logistic regression. Finally, it discusses the application of maximum likelihood estimation and the use of log likelihood for optimization.

What is the role of the sigmoid function in logistic regression?

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Explain the process of building a likelihood function in the context of logistic regression.

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Why is it beneficial to maximize the log of the likelihood function instead of the likelihood function itself?

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