Two Practical Ways to Improve an Algorithm
Question: What two practical levers can you usually pull first to make a learning algorithm perform better?
Sample answer: Use a larger model and train it on more data.
Key points:
- Increase the model's capacity.
- Add more training examples.
Rubric: The response must name both increasing model size and increasing the amount of data.
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Which two approaches are usually the most dependable ways to improve a supervised learning system's performance?
True or False: When a model is underfitting, the only dependable way to improve it is to collect more labeled examples.
A photo-tagging system is missing subtle patterns. What word completes this advice: increase the capacity of the _____ and train on more labeled examples?
Which pair is a dependable way to improve an ML system today?
True or False: In many modern machine learning settings, increasing model size and adding more training examples are dependable ways to improve performance.
A practical way to improve a machine learning system is to build a larger _____ and add more data.
Match each model-improvement strategy to its description.
Order the steps for using the two dependable ways to improve a model.
Why are larger models and more data often called dependable ways to improve performance?
True or False: Collecting more labeled data is usually an unreliable way to improve a model.
A practical way to improve a model is to use a larger network and more _____.
Match each training limitation to the most direct improvement lever.
Order the reasoning steps for when bigger models and more data are likely to help.
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Two Practical Ways to Improve an Algorithm