Understanding the bias-variance trade-off
In the journey of building machine learning models, understanding how well they perform on unseen data is paramount. Evaluating a model’s performance provides insights into its effectiveness, generalization capabilities, and potential areas for improvement. In this section, we delve into the critical process of using test sets to assess model performance comprehensively.
Model evaluation is a crucial step in the machine learning pipeline that validates the utility of a model in real-world scenarios. It gauges how well the model’s predictions align with actual outcomes, ensuring that the model can make accurate and reliable decisions beyond the training data. When assessing a model’s performance, it’s essential to consider two key aspects: bias and variance.
Bias refers to the error due to overly simplistic assumptions in the learning algorithm, leading to an underfit model that misses relevant relationships...
