Understanding The Bias-Variance Tradeoff In Machine Learning
In machine learning, understanding the bias-variance tradeoff is crucial for building robust models. It balances model complexity & prediction accuracy, ensuring algorithms perform well on both training & unseen data.
In the realm of machine learning, understanding the bias-variance tradeoff is essential for building robust models. This concept helps us navigate the balance between model complexity and prediction accuracy, ensuring that our algorithms perform well on both training and unseen data. Understanding Bias and Variance What is Bias? Bias refers to the error introduced when a model makes incorrect assumptions about the data. High bias can lead to underfitting, where the model is too simple to capture the underlying patterns in the data. For example, using a linear model to fit da...