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…
