6. Machine Learning Classification Part 2

ML Lectures Playlist: https://youtube.com/playlist?list=PLGWXNgjLi7BTp_T4HU-KkbHBerAE8gRp4&si=Jc00z8S92vhNuzlN Welcome to Dr. Holz's Advanced Lecture on Machine Learning Classification! 🎉 📚 What You’ll Learn in This Lecture: 🔍 Class Imbalance in Binary Classification: Understand the impact of imbalance, learn key metrics, and explore resampling, class weights, and cost-sensitive techniques to handle imbalanced data effectively. 🗂️ Overview of Multiclass and Multilabel Classification: Get a comprehensive understanding of handling multiple and simultaneous classes, with a focus on appropriate evaluation metrics like Hamming Loss, Macro F1, and Label Ranking Average Precision (LRAP). 📏 Ordinal Classification Essentials: Discover how to work with ordered classes and the importance of using specialized techniques and metrics, including Ordinal Logistic Regression, Mean Absolute Error (MAE), and Quadratic Weighted Kappa (QWK). 💻‍💻 Tools: Python, Scikit-Learn, Google Colab ✏️ Timestamps: 0:00:00 Introduction 0:01:43 Agenda 0:02:32 Introduction to Imbalanced Classification 0:03:10 Imbalanced Classification Evaluation Metrics 0:03:50 ROC Curve 0:05:40 PR Curve 0:07:44 Oversampling and Undersampling 0:09:09 SMOTE 0:11:09 Cost-Sensitive Learning 0:13:54 [Coding] Imbalanced Binary Classification 0:23:24 Introduction to Multiclass and Multilabel Classification 0:24:50 Multiclass Classification 0:29:56 [Coding] Multiclass Classification with Logistic Regression using One-vs-Rest and One-vs-One Strategies 0:37:28 Multilabel Classification 0:49:04 [Coding] Multilabel Classification with Logistic Regression and Evaluation Metrics 0:55:24 Ordinal Classification and Ranking 0:58:06 [Coding] Ordinal Classification with Ordinal Logistic Regression 1:04:20 Key Takeaways 1:06:17 Thank you 👍 Don’t forget to like, subscribe, and hit the 🔔 notification bell for more data science tutorials! #machinelearning #Classification #ClassImbalance #Multiclass #Multilabel #OrdinalClassification #Python #MLTutorials P.S.: Known bugs: 1. 25:46 - Small error on the slide: It should say Model 3: Lemon vs. Pineapple, with a proper illustration showing only lemons and pineapples clearly divided. 2. 26:59 - There is a typo in the Recall formula on slides 22 and 23. It should be TP/(TP + FN).