Отправлено: 15.02.25 10:16. Заголовок: What is the Bias-Variance Trade-Off?

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.


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

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.