Imagine a scenario in which a model works perfectly well with the data it was trained on, but provides incorrect predictions when it meets new, unfamiliar data. On the other hand, in certain cases, it struggles to grasp the intricacies of the data and thus fails to provide an accurate prediction.
Striking a balance between accuracy and the ability to make predictions beyond the training data in an ML model is called the bias-variance tradeoff.
In this article, we will explore what bias and variance are, and how they affect the performance of machine learning models. We’ll also discuss techniques for balancing these two parameters and see how they can be applied in ML modeling.
What is bias?
Bias in machine learning refers to the difference between a model’s predictions and the actual distribution of the value it tries to predict. Models with high bias oversimplify the data distribution rule/function, resulting in high errors in both the training outcomes and test data analysis results.
Bias is typically measured by evaluating the performance of a model on a training dataset. One common way to calculate bias is to use performance metrics such as mean squared error ( MSE) or mean absolute error ( MAE), which determine the difference between the predicted and real values of the training data.
Bias is a systematic error that occurs due to incorrect assumptions in the machine learning process, leading to the misrepresentation of data distribution.
The level of bias in a model is heavily influenced by the quality and quantity of training data involved. Using insufficient data will result in flawed predictions. At the same time, it can also result from the choice of an inappropriate model.
Watch this video for a more detailed explanation of how bias is measured:
