Classification with Regularized Logistic Regression
Summary
Well use a real-world survey data application and provide a step-by-step guide to implementing your own regularized logistic regression models using the GAUSS Machine Learning library, including: Logistic regression is a statistical method that can be used to predict the probability of an event occurring based on observed features or variables. One potential pitfall of logistic regression is its tendency for overfitting, particularly with high dimensional feature sets. Today we will use airline passenger satisfaction data to demonstrate logistic regression with regularization. Using the mean and standard deviation from the entire dataset, before the train/test split would allow information from the test set to "leak" into our model. Since the main purpose of regularization is to address overfitting the model to the training data, we dont have much reason to use it.
