How to understand the Bias-Variance tradeoff in machine learning?

Hi all,

I’m trying to wrap my head around the concept of the bias-variance tradeoff in machine learning. I understand that a model with high bias tends to underfit the data, while a model with high variance tends to overfit the data. However, I’m having trouble understanding how to strike the right balance between bias and variance to achieve optimal performance.

Can anyone provide a simple explanation of the bias-variance tradeoff and how it affects model performance? Additionally, are there any techniques or best practices for finding the optimal balance between bias and variance?

Here is the dataset that you can refer to:

Any help would be greatly appreciated. Thanks in advance!