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The balanced weight is a widely used method for imbalanced classification models. It penalizes the wrong predictions about the minority classes by giving more weight to the loss function. In this tutorial, we will talk about how to use balanced weight for the imbalanced multi-label classification. We will cover the following: 👉 What is the algorithm behind the balanced class weights for multi-classes? 👉 How to use class weights on random forest and logistic regression for multi-label classification? 👉 How to interpret the model performance metrics of a multi-label classification model? 👉 How to decide whether to use the balanced weights for an imbalanced multi-label classification model? ⏰ Timecodes ⏰ 0:00 - Intro 0:46 - Step 1: Import Libraries 1:13 - Step 2: Create an Imbalanced Dataset 2:21 - Step 3: Train Test Split 2:47 - Step 4: Baseline Multi-label Random Forest Classification 3:14 - Step 5: Multi-label Metrics Interpretation 5:13 - Step 6: Algorithm Behind Balanced Weights 6:30 - Step 7: Balanced Weights For Multi-label Random Forest Model 7:43 - Step 8: Baseline Multi-label Logistic Regression 8:32 - Step 9: Balanced Weights For Multi-label Logistic Regression Model 9:32 - Summary ❤️ Blog post with code for this video: / imbalanced-multi-label-classification-bala... 📒 Code Notebook: https://mailchi.mp/f7b746e97a09/wjxak... 🚛 GrabNGoInfo Machine Learning Tutorials Inventory: / grabngoinfo-machine-learning-tutorials-inv... 🏪 Purchase data science and computer science themed products in my Amazon store: https://amzn.to/40HUTsl 🎞️ Imbalanced dataset playlist: • Imbalanced Model & Anomaly Detection 📺 Videos mentioned in the video Balanced Weights For Imbalanced Classification: • Controlling Class Weights For Imbalanced C... 👩🏻💻 Follow me on LinkedIn: / grabngoinfo 🔥 Check out more machine learning tutorials on my website! https://grabngoinfo.com/tutorials/ 🛎️ SUBSCRIBE to GrabNGoInfo https://bit.ly/3keifBY 📧 CONTACT me at contact@grabngoinfo.com #multiclass #imbalanceddata #machinelearning #datascience