However, minimizing bias as much as possible can lead to more accurate and fair predictions. It’s important to note that some bias is inevitable in machine learning models. In that case, the model may consistently underestimate or overestimate the actual price, leading to a high bias. Suppose we use a linear regression model that is too simple and only considers the number of bedrooms as a feature.
In other words, bias occurs when a model cannot capture the complexity of the underlying data and instead relies on preconceived notions or limited information.įor example, suppose we have a regression problem where we are trying to predict the price of a house based on its features, such as the number of bedrooms, bathrooms, and square footage. In machine learning, bias refers to the tendency of a machine learning algorithm to consistently make predictions that are either higher or lower than the actual value. What is the importance of Bias and Variance?.Understanding Bias and Variance Trade-off.What effect does it have on the machine learning model?.
