Problem: Explain Bias vs Variance in machine learning

Topics to be covered:

1. Definition of Bias and Variance in Machine Learning
2. Understanding the Bias-Variance Trade-off
3. Techniques to measure and manage bias and variance
4. Dealing with underfitting and overfitting issues
5. The impact of bias and variance on model complexity
6. The role of regularization in reducing bias and variance
7. How bias and variance affect the accuracy and precision of predictive models
8. The concept of increasing model complexity to decrease bias