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.
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.
During a machine learning course in Pune, you’ll work on such practical projects, helping you understand how to balance bias and variance effectively.
Breaking Down Bias and Variance
1. 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).