Imbalanced data refers to datasets where the distribution of classes is heavily skewed, with one class significantly outnumbering the others. Dealing with imbalanced data is crucial as it can lead to biased models that perform poorly on minority classes. Addressing Class Imbalance with Undersampling, Oversampling, SMOTE, and Ensemble Methods. Imbalanced datasets pose challenges for machine learning models, but techniques like undersampling (reducing majority class samples), oversampling (increasing minority class samples), SMOTE (Synthetic Minority Oversampling Technique), and ensemble methods (combining multiple models) help mitigate bias and improve predictive performance on minority classes.

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⌚Time Stamps⌚

00:00 Intro
00:54 What is Imbalanced Data?
04:10 Problems with Imbalanced Data
08:00 Imbalanced Data Demo
11:13 Why studying imbalanced data is important?
16:58 Undersampling
25:56 Oversampling
31:06 SMOTE
42:43 Ensemble Learning
47:06 Cost Sensitive Learning

51:30 Other techniques