Question - What is a bias-variance trade-off in Data Science?
Answer -
When building a model using Data Science or Machine Learning, our goal is to build one that has low bias and variance. We know that bias and variance are both errors that occur due to either an overly simplistic model or an overly complicated model. Therefore, when we are building a model, the goal of getting high accuracy is only going to be accomplished if we are aware of the tradeoff between bias and variance.
Bias is an error that occurs when a model is too simple to capture the patterns in a dataset. To reduce bias, we need to make our model more complex. Although making the model more complex can lead to reducing bias, and if we make the model too complex, it may end up becoming too rigid, leading to high variance. So, the tradeoff between bias and variance is that if we increase the complexity, the bias reduces and the variance increases, and if we reduce complexity, the bias increases and the variance reduces. Our goal is to find a point at which our model is complex enough to give low bias but not so complex to end up having high variance.