Learn Before
Analyze the implications of a classifier exhibiting 15% bias and 1% variance.
Question: Given a classifier with an estimated bias of 15% and a variance of 1%, analyze its performance on the training and dev sets. Explain what these error rates indicate about the algorithm's state.
Sample answer: A classifier with 15% bias and 1% variance is fitting the training set poorly (15% error), meaning it is not capturing the underlying patterns of the data well. However, its error on the dev set is barely higher than the training error, as indicated by the low 1% variance. This combination indicates that the model has high bias but low variance, a condition which is commonly referred to as underfitting.
Key points:
- 15% bias indicates poor performance on the training set.
- 1% variance means dev set error is barely higher than training error.
- The classifier has high bias and low variance.
- This condition is known as underfitting.
Rubric: The essay should correctly identify the model's performance on both sets and conclude that the model is underfitting due to high bias and low variance.
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
A classifier has 15% training error and its dev set error is barely higher. How is this classifier best described?
In the high bias, low variance example, the gap between training error and dev set error is large.
A classifier with high bias and low _____ fails to fit the training set well and is described as underfitting.
Match each bias-variance term to its description in the Machine Learning Yearning example.
Arrange the steps for measuring bias and variance to diagnose a classifier.
What does an estimated variance of 1% (alongside 15% bias) specifically indicate about the classifier?
An underfitting classifier performs well on its training set but poorly on the dev set.
In the Machine Learning Yearning example, the estimated bias is _____ and the classifier is said to be underfitting.
Match each bias-variance condition to what it implies about the train-to-dev error gap.
Arrange the reasoning steps to conclude that a classifier with 15% bias and 1% variance is underfitting.
Analyze the implications of a classifier exhibiting 15% bias and 1% variance.
Diagnosing a medical imaging classifier with high training error.
What term describes an algorithm with high bias and low variance?