In the context of linear regression, what does 'overfitting' mean?
Practice Questions
1 question
Q1
In the context of linear regression, what does 'overfitting' mean?
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 parameters
The model is perfectly accurate
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.
Questions & Step-by-step Solutions
1 item
Q
Q: In the context of linear regression, what does 'overfitting' mean?
Solution: Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.
Steps: 6
Step 1: Understand that linear regression is a method used to predict outcomes based on input data.
Step 2: Know that a model is trained using a set of data called 'training data'.
Step 3: Realize that during training, the model tries to find patterns in the data.
Step 4: Overfitting happens when the model learns not just the patterns, but also the random noise in the training data.
Step 5: When a model is overfitted, it performs very well on the training data but poorly on new, unseen data.
Step 6: The goal is to create a model that captures the true patterns without getting distracted by noise.
