Overfitting in AI: What It Is and How to Avoid It
Introduction Have you ever trained an AI model that performed exceptionally well on your training data but struggled with new, unseen data? If so, you might have encountered the issue of overfitting. Overfitting is a common problem in artificial intelligence (AI) and machine learning, where a model learns the noise and details of the training data to the extent that it performs poorly on new data. According to a study by MIT, overfitting affects the reliability and generalizability of AI models, limiting their practical applications. In this article, we will explore what overfitting is, its causes, and effective strategies to avoid it. Section 1: Understanding Overfitting What is Overfitting? Overfitting occurs when an AI model becomes too complex and captures the noise and outliers in the training data rather than the underlying patterns. As a result, the model performs well on the training data but fails to generalize to new, unseen data. Investopedia explains that overfitting ...