As a machine learning and data science enthusiast, you’ve probably heard the terms bias and variance thrown around quite a bit. But what do these terms actually mean, and why are they so important? In this post, we’ll take a closer look at bias and variance, and discuss how to balance them for optimal performance in your models.
Bias refers to the difference between the predicted values of a model and the true values of the data. In simpler terms, it’s the degree to which a model’s predictions are consistently incorrect. For example, imagine you’re trying to predict the price of a car based on its features. A model with high bias might always predict the price to be lower than it actually is, regardless of the specific car.
On the other hand, variance refers to the variability of a model’s predictions for different training sets. In other words, it’s the degree to which a model’s predictions change depending on the specific data it’s trained on. For example, imagine…