What does a large validation gap indicate when training error is already high?
Question: What does it suggest when a model's training error is high and its validation error is much higher?
Sample answer: It suggests a variance problem in addition to the existing bias. The large gap means the model fits the training data better than the validation data.
Key points:
- Validation error is much larger than training error.
- That gap indicates variance.
- High training error means bias is also present.
Rubric: The answer should explicitly state that the validation error is much higher than the training error and that this points to variance.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Recognizing When Both Bias and Variance Are High
If a model has both high bias and high variance, its training error and development error are usually about the same.
Learning Curves That Point to _____
Reading Training and Validation Patterns
Diagnosing Bias and Variance in a Model
What a learning curve suggests when both underfitting and overfitting are present
Evaluating a Sales Forecast Model
What does a large validation gap indicate when training error is already high?
What should you do when the learning curve suggests both weak fit and high instability?
When a classifier has high bias, it usually achieves very low training error.