Learn Before
Using a Development-Set Curve to Estimate the Payoff of More Data
After marking the target performance level on a development-set error curve, extending the trend by eye can provide a rough estimate of whether a larger training set might close the gap. In the example, the projected shape suggested that doubling the data could plausibly bring performance to the target.
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
Constructing a Performance Curve from Different Training Set Sizes
Larger Training Sets Usually Lower Dev-Set Error
Target Error Rate on a Learning Curve
Using a Development-Set Curve to Estimate the Payoff of More Data
Using Training Error to Judge the Value of More Data
Why plot training and development error together on a learning curve?
Tiny Training Samples Make Learning Curves Unstable
What are the axes of a learning curve?
A learning curve shows error versus network depth.
Learning Curves and Validation Error
Parts of a learning curve
Constructing a learning curve for model selection
What a learning curve shows
Using a learning curve to judge whether more data will help
What a Learning Curve Shows
What belongs on the vertical axis of a learning curve?
A learning curve is less informative than a single validation score.
Learn After
What does sketching an extension of the dev-error curve help you estimate?
A trend line from past data can give an exact guarantee of the model’s future error after more training data is added.
Add the target _____ to a dev-error curve before estimating the value of more data.
Match each learning-curve element with its role when estimating the benefit of more labeled data.
Order the reasoning steps for judging whether more data is likely to reach a target performance level.
Using a Validation Curve to Judge the Value of More Data
If the validation curve reaches the target after the data set doubles, what should be concluded?
Why Add the Target Line to a Learning Curve?
Which conclusion best matches the example learning curve?
A rough learning-curve estimate can support a tentative prediction about the value of adding more training data, but it does not guarantee the outcome.