While looking to precesion

*P*and recall*R*(for example) we may be not able to choose the best model correctlySo 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 👩🏫

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 |

🤗 Satisficing Metric | A metric that just has to be |

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

thresholdthat we determine

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 ✨

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

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 👮

**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 🏳