Train/Dev/Test Set Distributions (C3W1L05) | Summary and Q&A

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August 25, 2017
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DeepLearningAI
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Train/Dev/Test Set Distributions (C3W1L05)

TL;DR

Setting up development and test sets from the same distribution is crucial for maximizing the efficiency of machine learning applications.

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Key Insights

  • 😫 The setup of development and test sets significantly impacts the progress and efficiency of machine learning teams.
  • 😫 Having development and test sets come from different distributions can waste months of work and hinder performance.
  • 😫 Choosing development and test sets that reflect future data and putting them from the same distribution can maximize efficiency.
  • 😤 A clear evaluation metric is essential, as it helps the team aim for the desired target.
  • 😫 The size of the development and test sets also plays a role in maximizing efficiency and may vary depending on the context.
  • 🥺 Following these guidelines can save machine learning teams months of work and lead to better results.
  • 📼 The training set setup will be discussed in a separate video, but it is crucial to align it with the development and test sets.

Transcript

the way you set up your training general development sets and test sets can have a huge impact on how rapidly you or your team can make progress on building machine learning application let's see in team even teams and very large companies set up these data sets in ways that really slowed down rather than speeds up the progress of the team let's ta... Read More

Questions & Answers

Q: Why is it important to set up separate development and test sets in machine learning?

Development and test sets allow the team to evaluate different ideas, iterate quickly, and choose the best classifier. It helps improve performance and ensures the model is effective before deploying it.

Q: What happens when the development and test sets come from different distributions?

When the data in the development and test sets are from different distributions, optimizations made on the development set may not translate to good performance on the test set. This can lead to wasted time and ineffective models.

Q: How can teams avoid wasting time optimizing for the wrong target?

To avoid this, it is recommended to have both the development and test sets come from the same distribution as the data expected in the future. This ensures that optimizations made on the development set will carry over to the test set.

Q: How can data from different income zip codes impact model performance?

The specific example of loan approvals shows that if the development set is comprised of loan applications from medium income zip codes, but the test set is from low-income zip codes, the model's performance may not translate well. The distribution of data plays a crucial role in model effectiveness.

Summary & Key Takeaways

  • Setting up a development set (or depth set) and a test set is essential for evaluating models and improving performance.

  • Using data from different distributions in the development and test sets can hinder progress and lead to poor performance.

  • It is important to choose a development and test set that reflects the data expected in the future, ensuring the team is aiming at the right target.

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