Learn Before
Using a Dev Set to Compare Model Versions
If a team has no development set or single evaluation score, it may have to deploy each new classifier into the product and test it by hand to decide whether it is better. A development set and one metric make it much easier to spot small gains or losses, compare candidate ideas quickly, and decide which changes are worth continuing and which should be dropped.
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
Precision for a Cat Detector
Recall for a Positive-Class Detector
One Score from Precision and Recall
Averaging Several Metrics into One Score
Using a Dev Set to Compare Model Versions
What is the main advantage of using one evaluation score while developing models?
A single score can help a team rank many models quickly.
Single-number metrics for model selection
Match each development choice to its role in testing model ideas.
Put the model-selection process with one metric in order.
Why a single evaluation score speeds model development
Use one primary score to compare many candidate models.
What two kinds of guidance does a single-number score provide?
What evaluation strategy best helps you choose quickly among many model candidates?
A single evaluation score can help a team choose among competing models and point the team toward the next improvement.
Learn After
If a team has no dev set and no evaluation metric, how do they judge whether a new classifier is better?
A single validation score can help a team quickly tell whether a new model idea gives a small gain or a large gain.
A development set and metric help a team decide which ideas to keep _____ and which to drop.
Match each evaluation setup with its practical consequence when comparing classifier versions.
Order the actions a team should take when no dev set or metric exists for a new classifier.
What practical advantage do a development set and an evaluation metric give a machine learning team?
Manually trying every new classifier by using the app is usually a fast way to evaluate model improvements.
When there is no dev set or metric, each new classifier has to be _____ into the product before the team can judge whether it is better.
Match each evaluation concept to its role in comparing classifier versions.
Using a dev set to compare a new classifier idea
Why a Validation Metric Beats Ad Hoc Product Testing
Evaluating New Versions of a Parcel Sorting Classifier
How Evaluation Data Changes Model Selection