When a machine learning model performs poorly on unseen data but perfectly on its training data, what specific issue is it suffering from?

The machine learning model is suffering from overfitting. Overfitting occurs when a model learns the training data too precisely, memorizing not just the underlying general patterns but also the noise, specific examples, and irrelevant details unique to that particular training dataset. The training data refers to the specific dataset the model was given to learn from and adjust its internal parameters. When a model overfits, it achieves nearly perfect performance on this training data because it has essentially memorized it. However, this memorization comes at the cost of generalization, which is the model's ability to accurately predict or classify on new, previously unseen data. Unseen data, also known as test or validation data, is data the model has never encountered during its learning process, used to evaluate how well it can apply its learned patterns to real-world scenarios. Because the overfitted model learned the specific nuances and noise of its training data rather than the broader, transferable rules, these specific details do not exist or are different in the unseen data. Consequently, its performance on this unseen data is poor. The 'noise' in data refers to irrelevant information, random errors, or variations that are not part of the true underlying relationship the model should learn. An overfitted model mistakes this noise for meaningful patterns, leading to poor performance when faced with any data that differs from its exact training examples.