CST 383 - Week 7

In this lecture, I learned about logistic regression and how it functions as a classification model. A key insight was understanding that logistic regression can be viewed as linear regression combined with a sigmoid “squashing” function, which transforms the output into a probability between 0 and 1.
I learned how to describe the model both mathematically and conceptually. It computes a linear combination of input features and applies the sigmoid function to estimate the probability of a binary outcome.
I also learned how to select the best logistic regression model using training data and evaluate its performance using appropriate metrics.
For homework, I implemented logistic regression using Scikit-Learn to predict customer churn. I trained the model, generated predictions, and evaluated its performance. This hands-on practice helped me connect the theoretical concepts to practical implementation.

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