Avoid these common mistakes when performing cross-validation in Python

A vital technique for evaluating the performance of machine learning models is cross-validation which involves partitioning a dataset into subsets to train and test the model iteratively. However, there are some common mistakes people make when performing cross-validation in Python, and in this thread, we will discuss a few of them with their correct implementations.

1. Using default shuffle in cross-validation:

Some cross-validation functions shuffle the data by default and this may lead to data leakage if the temporal or spatial order of your data matters. An example of such data would be time-series data in which the temporal order of the data matters and in such cases, you don’t necessarily want to shuffle. Here is an example code that you can use to turn off the default shuffle parameter:

2. Ignoring stratified sampling for imbalanced data:

Datasets are often imbalanced where one class is more frequent than the other(s), when applying cross-validation to such datasets, it’s crucial to maintain the class distribution in each fold to ensure unbiased model performance. Ignoring this consideration and performing standard random sampling can lead to misleading performance estimates. Here is an example code that uses stratified sampling to tackle this mistake ensuring equal class distribution in each fold:

3. Using the wrong evaluation metric:

Another common mistake that occurs is selecting an inappropriate evaluation metric for cross-validation scores. This can lead to skewed performance estimation, especially for imbalanced datasets, and can cause misleading results. The code below shows how you can make your scorer and specify it as the scoring metric for cross-validation.