<ul><li>Logistic Regression serves as a fundamental concept in probabilistic classification, complementing Linear Regression in modeling relationships.</li><li>Building a Logistic Regression model from scratch using Python and NumPy for a #100DaysOfAI project allows for a deeper understanding of the algorithm's inner workings and decision-making processes.</li><li>Emphasis on key considerations such as precision and recall led to the evaluation of the model's performance using the F2 score, beneficial for certain classification tasks.</li><li>Visualizations of the decision boundary through filled contour plots offer insights into how the model separates classes, showcasing the practical application and effectiveness of the custom Logistic Regression implementation.</li></ul>

Laying the Foundation: A Ground-Up Logistic Regression

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