What is overfitting in machine learning? 

Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise or random fluctuations. This leads to a model that performs well on the training data but poorly on new, unseen data.

Characteristics of Overfitting:

1. High Training Accuracy: The model achieves very high accuracy on the training data.
2. Low Generalization: The model performs poorly on validation or test data, indicating it has not generalized well to new data.
3. Complex Model: The model may be overly complex with too many parameters or features, capturing noise rather than the signal.