Machine Learning: Bias VS Variance

The video tutorial discusses the importance of balancing bias and variance in machine learning models. It explains supervised learning, the bias-variance tradeoff, and how model complexity affects performance. Techniques like regularization are introduced to optimize models and prevent overfitting or underfitting.

Explain the bias-variance tradeoff in the context of machine learning.

What is the importance of preparing sufficient data examples in machine learning?

Describe the process of splitting a dataset into training and testing sets.

What is the significance of measuring the error of unseen data?

Discuss how model complexity relates to bias and variance.

What does it mean for a model to overfit or underfit?

How can regularization help in improving a machine learning model?