Handling imbalanced dataset in machine learning | Deep Learning Tutorial 21 (Tensorflow2.0 & Python)

codebasics · Beginner ·📐 ML Fundamentals ·5y ago

Skills: ML Maths Basics60%

Credit card fraud detection, cancer prediction, customer churn prediction are some of the examples where you might get an imbalanced dataset. Training a model on imbalanced dataset requires making certain adjustments otherwise the model will not perform as per your expectations. In this video I am discussing various techniques to handle imbalanced dataset in machine learning. I also have a python code that demonstrates these different techniques. In the end there is an exercise for you to solve along with a solution link. Code: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blo…

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Chapters (12)

Overview

0:01 Handle imbalance using under sampling

2:05 Oversampling (blind copy)

2:35 Oversampling (SMOTE)

3:00 Ensemble

3:39 Focal loss

4:47 Python coding starts

7:56 Code - undersamping

14:31 Code - oversampling (blind copy)

19:47 Code - oversampling (SMOTE)

24:26 Code - Ensemble

35:48 Exercise

Abstracting with Functions