cross-validation
Cross-validation is a method used to assess how well a model will perform on new, unseen data. Imagine you have a set of test questions and you want to ensure you understand the material. Instead of just studying and testing on the same questions, you can break your study materials into parts: use some to learn and others to test your knowledge. By rotating which parts you use for studying and testing multiple times, you get a clearer picture of your true understanding. This process helps prevent overfitting, ensuring the model is reliable and generalizes well.
Additional Insights
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Cross-validation is a technique used to evaluate how well a model performs on new, unseen data. It works by dividing a dataset into multiple parts. The model is trained on some parts and tested on others, rotating this process so every part gets a chance to be both training and testing data. This helps ensure that the model is robust and not just tailored to the specific dataset it was trained on. By using cross-validation, we can get a better understanding of a model's reliability and its ability to make accurate predictions in real-world scenarios.
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Cross-validation is a technique used to evaluate how well a model performs. Instead of testing it only on one set of data, we divide our data into several groups. The model is trained on some groups and then tested on another. This process is repeated multiple times, with different groups used for training and testing each time. By averaging the results, we get a clearer picture of the model's accuracy and reliability. This helps ensure the model works well not just on the data it was trained on, but also on new, unseen data.