Learn Before
Add a target line to a learning curve for a churn-prediction project.
Case context: A team is building a customer-churn classifier for a subscription app. They track dev-set error over time with a learning curve and have a performance level they hope to reach.
Question: How should that hoped-for performance level be used when reviewing the learning curve?
Sample answer: Plot the desired performance level on the same learning curve so it serves as a visible target for comparison with the dev-set error.
Key points:
- The desired error rate is the level the team wants the model to reach.
- Showing it on the learning curve makes the gap to the current dev-set error easy to see.
Rubric: The answer must state that the desired performance level should be placed on the learning curve as a reference 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
Using Experience to Set a Target Error Rate
The Best Human Benchmark Depends on How Accurate the System Already Is
What does the term desired error rate mean?
True or False: A performance target can be drawn on a learning curve.
A target error rate is the level of error a model is hoped to eventually _____ .
Match each term to its role in target-error analysis.
Arrange the steps for adding a target error level to a model’s learning curve.
On which plot should a target performance level be marked?
True or False: A desired error rate is the level of error a model has already reached on the development set.
The target performance should be added to your _____.
Match each element on a performance plot to its meaning.
Put the desired error rate workflow in order
Using a Target Error Rate on a Learning Curve
Add a target line to a learning curve for a churn-prediction project.
Adding a target performance level to a learning plot