Terminology for High Variance Generalization Failure
Question: In the cat classification example, what term is used to describe a model that has very low training error but fails to generalize to the development set?
Sample answer: This phenomenon is called overfitting.
Key points:
- Overfitting is the correct term.
- It corresponds to low training error and failing to generalize.
Rubric: The learner must correctly provide the term "overfitting".
0
1
Tags
Machine Learning
Deep Learning
Machine Learning Strategy
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Yearning @ DeepLearning.AI
Related
In the cat classification example, if training error is ~1% and dev error is 11%, what is the estimated variance?
A classifier with high variance has very low training error but fails to generalize to the dev set.
When a classifier has very low training error but high dev error, this is also called _____.
Match each metric or concept to its correct description in the cat classification bias-variance example.
Order the steps used in ML Yearning to diagnose high variance in the cat classification example.
Which pattern of training and dev set errors characterizes a high variance classifier in the cat example?
In ML Yearning's cat classification example, a classifier diagnosed with high variance has high training error.
In the cat classification example, variance is estimated as _____ minus training error.
Match each term to its role in diagnosing high variance from the cat classification example.
Order the reasoning steps to conclude the cat classifier is overfitting based on its error metrics.
Analyzing the Cat Classifier's Error Metrics
Diagnosing Overfitting in a Cat Image Recognizer
Terminology for High Variance Generalization Failure