Vinod Chugani explains the benefit of cross-validation in a data science project:
Many beginners will initially rely on the train-test method to evaluate their models. This method is straightforward and seems to give a clear indication of how well a model performs on unseen data. However, this approach can often lead to an incomplete understanding of a model’s capabilities. In this blog, we’ll discuss why it’s important to go beyond the basic train-test split and how cross-validation can offer a more thorough evaluation of model performance. Join us as we guide you through the essential steps to achieve a deeper and more accurate assessment of your machine learning models.
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