What Is Human-Level Performance in Machine Learning?

TL;DR

Human-level performance in machine learning is defined as a proxy for Bayes error, with benchmarks varying from 3% for untrained humans to 0.5% for a team of experienced doctors. The definition you use should align with your goals, whether that’s to exceed an individual’s performance or to estimate the best possible error rate.

Transcript

the term human level performance is sometimes use casually in research articles but let me show you how we can define a little bit more precisely and in particular use the definition that the phrase human level performance that is most useful for helping you drive progress in your machine learning project so remember from our last video that one of... Read More

Key Insights

• 🛟 Human level error serves as a useful proxy for estimating Bayes error in machine learning projects.
• 🎚️ The definition of human level error depends on the purpose of the analysis, whether it is to surpass a single human or estimate Bayes error.
• 😈 Comparing human level error to training error and dev error helps identify the relative importance of bias and variance in a learning algorithm.
• 🎚️ As performance approaches human level performance, it becomes more challenging to analyze bias and variance effects accurately.

Questions & Answers

Q: How is human level performance useful in estimating Bayes error?

Human level performance serves as a proxy or estimate for Bayes error, which is the best possible error any function could achieve. It helps in understanding the limits of performance and making decisions on bias and variance reduction.

Q: What is the definition of human level error in the context of surpassing a single human's performance?

If the goal is to surpass a single human's performance, human level error can be defined as the error rate of a typical doctor or an experienced doctor. This definition determines if a system is good enough to be deployed in certain contexts.

Q: How can human level error be used for analyzing bias and variance?

By comparing human level error to training error and dev error, an estimate of avoidable bias and variance can be obtained. The difference between human level error and training error measures the bias, while the difference between training error and dev error indicates the variance.

Q: Why does the estimation of human level error become more challenging as performance improves?

As performance approaches human level performance, it becomes harder to distinguish between bias and variance effects. The accuracy of estimating Bayes error decreases, making it difficult to determine the focus of improvement in a machine learning project.

Summary & Key Takeaways

• The term "human level performance" is defined as a proxy or estimate for Bayes error in machine learning projects.

• The example of medical image classification demonstrates different levels of human performance, ranging from 3% error for untrained humans to 0.5% error for a team of experienced doctors.

• The definition of human level error depends on the purpose of the analysis, either surpassing a single human's performance or estimating Bayes error.