👩‍🔧 Notes on Structuring Machine Learning Projects

Make your training procedure more effective

✨ How to effectively set up evaluation metrics?

• While looking to precesion P and recall R (for example) we may be not able to choose the best model correctly

  • So we have to create a new evaluation metric that makes a relation between P and R

  • Now we can choose the best model due to our new metric 🐣

  • For example: (as a popular associated metric) F1 Score is:

    • $F1 = \frac{2}{\frac{1}{P}+\frac{1}{R}}$

To summarize: we can construct our own metrics due to our models and values to be able to get the best choice 👩‍🏫

📚 Types of Metrics

For better evaluation we have to classify our metrics as the following:

Metric Type	Description
✨ Optimizing Metric	A metric that has to be in its best value
🤗 Satisficing Metric	A metric that just has to be good enough

Technically, If we have N metrics we have to try to optimize 1 metric and to satisfice N-1 metrics 🙄

🙌 Clarification: we tune satisficing metrics due to a threshold that we determine

🚀 How to set up datasets to maximize the efficiency

• It is recommended to choose the dev and test sets from the same distribution, so we have to shuffle the data randomly and then split it.

• As a result, both test and dev sets have data from all categories ✨

👩‍🏫 Guideline

We have to choose a dev set and test set - from same distribution - to reflect data we expect to get in te future and consider important to do well on

🤔 How to choose the size of sets

• If we have a small dataset (m < 10,000)

  • 60% training, 20% dev, 20% test will be good

• If we have a huge dataset (1M for example)

  • 99% trainig, %1 dev, 1% test will be acceptable

    And so on, considering these two statuses we can choose the correct ratio 👮‍

🙄 When to change dev/test sets and metrics

Guideline: if doing well on metric + dev/test set and doesn't correspond to doing well in the real world application, we have to change our metric and/or dev/test set 🏳