Optimizing Model Performance through Bias-Variance Trade-Off: Strategies for Accurate and Reliable Machine Learning

In this article, we explore the critical concepts of bias and variance, their impact on model performance, and effective strategies for achieving accurate and reliable predictions. It is important to strike the right balance between simplicity and complexity to build models that generalize well to new data.

1. BIAS

• Error caused by making assumptions or simplifications about the data to facilitate the approximation of the target function.
• Represents the amount by which a model’s predictions deviate from the true target value when compared to the training data.
• When bias error is introduced, the algorithm may learn quickly, but the predictions are not reliable. In other words, the model is unable to capture the true underlying patterns in the data.
• This often results in an underfit model, which is a model that is too simplistic to accurately represent the complexity of the data.

EXAMPLE

For example, let’s consider a linear regression model attempting to predict housing prices based on features such as size, number of bedrooms, and location. If the model assumes a…