The bias-variance trade-off is a key consideration in machine learning that affects how well a model generalizes to unseen data. It represents the balance between two types of errors:
Bias Error (Underfitting) – Occurs when a model is too simple and fails to capture the underlying patterns in the data.
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Variance Error (Overfitting) – Occurs when a model is too complex and captures noise along with actual patterns, making it perform poorly on new data.
A well-balanced model should neither be too biased nor too variant, ensuring it generalizes well to new data without being overly complex.
What is Bias?
Bias refers to the assumptions a model makes about the data to simplify learning. A high-bias model is too simplistic and fails to learn the true relationships within the dataset.
Characteristics of High-Bias Models:
✔ They rely on strong assumptions.
✔ They oversimplify relationships in data.
✔ They perform poorly on both training and test data (underfitting).