Exercise: Undersampling

Learn how to reduce the imbalance to a fixed ratio with NearMiss.

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Let’s get more familiar with the NearMiss undersampling strategy by practice.


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 sampling strategy to end up with a 1:10 ratio.

  1. Configure NearMiss version 3 and choose parameters to meet a 1:10 imbalance ratio after undersampling the majority class.

  2. Apply the sampling strategy on X_train and y_train to create undersampled training data.

  3. Verify that the undersampled data meets the 1:10 imbalance ratio.

Coding workspace

The X_train and y_train training data is available in memory in the workspace. Let’s try to code the solution.

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