Python Natural Language Processing

Bias-variance trade-off

Here, we will look at a high-level idea about the bias-variance trade-off. Let's understand each term one by one.

Let's first understand the term bias. When you are performing training using an ML algorithm and you see that your generated ML-model doesn't perform differently with respect to your first round of training iteration, then you can immediately recognize that the ML algorithm has a high bias. In this situation, ML algorithms have no capacity to learn from the given data so it's not learning new things that you expect your ML algorithm to learn. If your algorithm has very high bias, then eventually it just stops learning. Suppose you are building a sentiment analysis application and you have come up with the ...