8 Simple Techniques to Prevent Overfitting by David Chuan-En Lin

overfitting  Abstract Overfitting is a fundamental issue in supervised machine learning which prevents us from perfectly generalizing the models to well fit observed data Overfitting occurs when a model becomes too closely adapted to the training data, capturing even its random fluctuations Imagine teaching a child to recognize

Conclusions and Recommendations Overfitting is definitely a risk also in LLMs, especially in larger models However, its impact strongly Conclusion Overfitting happens when a model fits training data too closely, resulting in great training performance but poor generalization

Overfitting can lead to misleading results and poor decision-making, while underfitting can result in models that fail to capture important patterns and In statistics and machine learning, overfitting occurs when a model tries to predict a trend in data that is too noisy Overfitting is the result of an overly