👩🏫 Implementation Guidelines
Implementation guidelines and error anlysis
Last updated
Implementation guidelines and error anlysis
Last updated
Well, in this stage we have a criteria, is your model doing worse than humans (Because humans are quite good at a lot of tasks 👩🎓)? If yes, you can:
👩🏫 Get labeled data from humans
👀 Gain insight from manual error analysis; (Why did a person get this right? 🙄)
🔎 Better analysis of bias / variance 🔍
🤔 Note: knowing how well humans can do on a task can help us to understand better how much we should try to reduce bias and variance
Processes are less clear 😥
Suitable techniques will be added here
Let's assume that we have these two situations:
Even though training and dev errors are same we will apply different tactics for better performance
In Case1, We have High Bias so we have to focus on bias reduction techniques 🤔, in other words we have to reduce the difference between training and human errors the avoidable error
Better algorithm, better NN structure, ......
In Case2, We have High Variance so we have to focus on variance reduction techniques 🙄, in other words we have to reduce the difference between training and dev errors
Adding regularization, getting more data, ......
We call this procedure of analysis Error analysis 🕵️
In computer vision issues,
human-level-error ≈ bayes-errorbecause humans are good in vision tasks
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Product recommendations
Logistics
Loan approvals
.....
When we have a new project it is recommended to produce an initial model and then iterate over it until you get the best model, this is more practical than spending time building model theoretical and thinking about the best hyperparameter -which is almost impossible 🙄-
So, just don't overthink! (In both ML problems and life problems 🤗🙆)
Term
Description
👩🎓 Bayes Error
The lowest possible error rate for any classifier (The optimal error 🤔)
👩🏫 Human Level Error
The error rate that can be obtained by a human
👮♀️ Avoidable Bias
The difference between Bayes error and human level error
Case1
Case2
Human Error
1%
7.5%
Training Error
8%
8%
Dev Error
10%
10%