What does the term 'overfitting' refer to in model evaluation?
Practice Questions
1 question
Q1
What does the term 'overfitting' refer to in model evaluation?
Model performs well on training data but poorly on unseen data
Model performs poorly on both training and unseen data
Model performs well on unseen data but poorly on training data
Model has high bias
Overfitting occurs when a model learns the training data too well, resulting in poor generalization to unseen data.
Questions & Step-by-step Solutions
1 item
Q
Q: What does the term 'overfitting' refer to in model evaluation?
Solution: Overfitting occurs when a model learns the training data too well, resulting in poor generalization to unseen data.
Steps: 5
Step 1: Understand that a model is a mathematical representation used to make predictions based on data.
Step 2: Know that training data is the information we use to teach the model.
Step 3: Realize that overfitting happens when the model memorizes the training data instead of learning the underlying patterns.
Step 4: Recognize that a model that overfits performs very well on the training data but poorly on new, unseen data.
Step 5: Conclude that overfitting is a problem because it means the model cannot generalize its knowledge to make accurate predictions in real-world situations.
