Machine Learning and Prediction – Part A (FRM Part 1 2025 – Book 2 – Chapter 15)

Master FRM Part 1 (Book 2, Chapter 15) Machine Learning & Prediction Part A. Jim explains when linear regression breaks down and how logistic regression fixes probability predictions. You’ll also learn categorical encoding, overfitting vs. underfitting, and regularization with Ridge, LASSO, and Elastic Net, plus how MLE and the sigmoid (logit) turn outputs into 0–1 probabilities. What you’ll learn: Linear vs logistic regression (classification) Maximum likelihood and decision thresholds Encoding categorical variables Regularization with Ridge, LASSO, Elastic Net Bias variance trade off and model selection For FRM (Part I & Part II) video lessons, study notes, question banks, mock exams, and formula sheets covering all chapters of the FRM syllabus, click on the following link: https://analystprep.com/shop/unlimite... AnalystPrep is a GARP-Approved Exam Preparation Provider for FRM Exams After completing this reading you should be able to: Explain the role of linear regression and logistic regression in prediction. Understand how to encode categorical variables. Discuss why regularization is useful and distinguish between the ridge regression and LASSO approaches. #FRM #MachineLearning #QuantitativeAnalysis #LogisticRegression #Ridge #LASSO #GARP #AnalystPrep