Solution: Undersampling
Learn how to apply NearMiss version 3 undersampling strategy to address severe class imbalance in binary classification tasks for entity resolution. This lesson guides you to configure sampling parameters for a 1 to 10 class ratio, perform data resampling, and verify the results, enhancing your model's training effectiveness.
We'll cover the following...
We'll cover the following...
Let’s get more familiar with the NearMiss undersampling strategy by practice.
Task
Here, we deal with severely imbalanced training data for a binary classification problem. By default, NearMiss will balance out the training data so that we have (roughly) a 1:1 ratio of classes. We want you to apply a slightly different ...