What is 'Overfitting' in Machine learning?

Solution(By Examveda Team)

What is 'Overfitting' in Machine Learning?

Option A: When a statistical model describes random error or noise instead of
Overfitting in machine learning occurs when a statistical model fits the training data too closely, capturing random noise or error in the data instead of the underlying patterns. So, Option A is a correct description of overfitting.

Option B: Robots are programmed so that they can perform the task based on data they gather from
Option B does not describe overfitting in machine learning. It appears to be unrelated to the concept of overfitting.

Option C: While involving the process of learning 'overfitting' occurs.
Option C is a vague statement that mentions overfitting but does not provide a clear explanation. It lacks specificity in describing the concept.

Option D: A set of data is used to discover the potentially predictive relationship
Option D does not accurately describe overfitting. It seems to be related to the general process of using data to discover predictive relationships but does not address the issue of overfitting.

In summary, overfitting in machine learning occurs when a statistical model fits the training data too closely, capturing random error or noise instead of the underlying patterns, as described in Option A.