Benchmarking Machine Learning Models with Cross-Validation and Matplotlib in Python

In this article, we will look at how to use Python to compare and evaluate the performance of machine learning models. We will use cross-validation with Sklearn to test the models and Matplotlib to display the results. The main motivation for doing this is to have a clear and accurate understanding of model performance and thus improve the model selection process. Cross-validation is a robust method for testing models on data other than training data. It allows us to evaluate model performance on folds, data that has not been used to train the model itself, which gives us a more accurate estimate of model performance on real data.