CST383 - Module 7
What did I learn in the seventh week of CST383? This week covered logistic regression, encoding categorical variables, and overfitting with learning curves. The main theme for this week was preparing data for machine learning, while choosing an appropriate model based on what is getting trained/tested. The most surprising aspect was how logistic regression works as a classification algorithm. Basically, logistic regression squishes a linear model's output using the sigmoid function, which makes it so that the probability is produced between 0 and 1. Therefore, does logistic regression simply inherit from linear regression, whether good or bad? A concept I found confusing was the bias-Variance trade off. I understood from the lecture video and the lecture slide (overfitting1.pptx) that high variance means that the model is too sensitive to training data, which creates a high bias that won't capture a realistic shape of the data. However, how do you know when a gap between train...