Random Train/Test Splits Can Be Misleading When Deployment Data Differs
A random 70%/30% split can give misleading results if the data used for training and testing does not match the distribution the system will face after deployment.
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
Do Not Randomly Mix Sources When Building Validation and Test Sets
Use a Small Amount of Target-Distribution Data in Training with Auxiliary Data
When to Reduce the Influence of Extra Training Data
Training-Development Set
Comparing Errors Across Two Data Sources
Training–Dev Distribution Gap
Domain adaptation solves only certain distribution-shift problems
Choosing Development and Test Data for a Specific Application
Factory Photos and Inspection Tablet Images as a Distribution Mismatch Example
Random Train/Test Splits Can Be Misleading When Deployment Data Differs
Which data should define the dev and test sets for the plant-disease app?
Development and test examples should be drawn to match the kind of data the deployed system is expected to see.
Development and test sets should match the kind of data you expect later.
Match each data group for the plant-disease app with its role.
Order the dataset choices when training data and evaluation data come from different sources.
Why training data and evaluation data may come from different sources
Find the evaluation-set mismatch in a voice-command detector.
Why did strong desktop-photo results not predict mobile-upload success?
Choosing Training and Evaluation Data from Two Image Sources
If training uses extra web-sourced photos, the dev and test sets must also contain web-sourced photos.
Learn After
Why can a random 70/30 train/test split be misleading in deployed machine-learning systems?
True or False: A random 70/30 split always creates a test set that matches the deployment distribution.
A Common Historical Train/Test Split
Match each data situation to whether a random 70/30 split is a good choice.
Order the steps for deciding whether a random split is appropriate.
Why a Random Split Can Mislead Under Distribution Shift
Diagnose a training split for a retail shelf classifier.
When is a random train/test split a poor evaluation choice?
When Can a Random 70/30 Split Be Misleading?
True or False: A 70/30 train/test split was a common default when datasets were much smaller than they are today.