Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Generali

The video discusses regularization in machine learning, emphasizing its role in controlling model flexibility by constraining parameter values. It explores how to evaluate a model's generalization ability on unseen data, highlighting the importance of splitting data into training and validation sets. The video explains overfitting, where a model performs well on training data but poorly on unseen data, and suggests strategies to mitigate it, such as using more data or simpler models. It also addresses the challenge of balancing data allocation for training and validation to ensure accurate model evaluation.

What is the purpose of regularization in machine learning models?

How can we evaluate the generalization of a model?

What does unseen data refer to in the context of model training?

What does overfitting mean in the context of machine learning?

What should be done if the model performs well on training data but poorly on validation data?

Why is it important to reserve a portion of data for validation?

How can one avoid overfitting when training a model?

What is the relationship between training data size and model performance?

What is the significance of having a larger validation set?

What are some common heuristics for splitting training and validation data?