Insufficient Training Data: Challenges and Solutions in Machine Learning

When the training sample data is insufficient, machine learning models may struggle to generalize well to unseen data. This can lead to overfitting, where the model performs well on the training data but poorly on new data. To address this challenge, various techniques can be employed, including data augmentation, transfer learning, and using ensemble methods. Data augmentation involves creating synthetic data based on existing samples, while transfer learning leverages pre-trained models on large datasets. Ensemble methods combine multiple models to improve robustness and reduce overfitting.