Why Overfitting occurs in TensorFlow Models?

Overfitting can be caused by several factors, including:

1. Complex Model Architecture: If the model is too complex relative to the amount of training data available, it can memorize the training data rather than learn generalizable patterns.
2. Insufficient Training Data: If the training dataset is small, the model may not capture enough variability in the data, leading to overfitting.
3. Lack of Regularization: Without regularization techniques like dropout, L1/L2 regularization, or early stopping, the model may overfit by not penalizing overly complex weights.
4. Data Mismatch: If there are significant differences between the training and test datasets (e.g., different distributions, noise levels), the model may struggle to generalize.

Overfitting occurs when a machine learning model learns to perform well on the training data but fails to generalize to new, unseen data. In TensorFlow models, overfitting typically manifests as high accuracy on the training dataset but lower accuracy on the validation or test datasets. This phenomenon happens when the model captures noise or random fluctuations in the training data as if they were genuine patterns, leading to poor performance on unseen data.