Improving Model Generalization with Cross-Validation: A Deep Dive
Introduction In machine learning, a high accuracy on the training set does not always mean a good model . A model may perform well on training data but fail to generalize to unseen data—this is known as overfitting . To solve this, cross-validation is one of the most effective techniques. It ensures that the model is evaluated on different subsets of data, providing a better estimate of performance and improving generalization . In this blog, we will explore: What cross-validation is and why it is important? Different types of cross-validation techniques. How to implement cross-validation in Python (Scikit-Learn & TensorFlow)? Best practices for improving model performance. 1. What is Cross-Validation? Cross-validation (CV) is a resampling technique that helps evaluate a model by splitting the dataset into multiple training and validation sets . Instead of training the model on a single training set and testing it once, CV allows multiple training-validation cycles, providing...