In a regression problem, what does the term 'overfitting' refer to?
Practice Questions
1 question
Q1
In a regression problem, what does the term 'overfitting' refer to?
The model performs well on training data but poorly on unseen data
The model is too simple to capture the underlying trend
The model has too few features
The model is perfectly accurate
Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on new data.
Questions & Step-by-step Solutions
1 item
Q
Q: In a regression problem, what does the term 'overfitting' refer to?
Solution: Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on new data.
Steps: 5
Step 1: Understand that a regression problem involves predicting a value based on input data.
Step 2: Know that a model is trained using a set of data called training data.
Step 3: Realize that overfitting happens when the model learns the training data too well.
Step 4: Recognize that this means the model captures not just the important patterns, but also the random noise in the data.
Step 5: Understand that because of this, the model performs poorly when it encounters new, unseen data.
