Logistic Regression & Cross-Entropy

Logistic regression is a very popular machine learning algorithm that is primarily used for binary classification tasks. It is a model that applies the logistic (or sigmoid) function to a linear combination of input features to produce a probability estimate for class membership. With its simplicity and interpretability, logistic regression has found widespread applications in various domains, including healthcare, marketing, finance, and social sciences. In this article, we will explore logistic regression in detail, discussing important concepts such as the sigmoid function, log likelihood and cross-entropy.

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