Machine Learning Skepticism

Julia Evans gives reasons to tamp down expectations with machine learning:

When explaining what machine learning is, I’m giving the example of predicting the country someone lives in from their first name. So John might be American and Johannes might be German.

In this case, it’s really easy to imagine what data you might want to do a good job at this — just get the first names and current countries of every person in the world! Then count up which countries Julias live in (Canada? The US? Germany?), pick the most likely one, and you’re done!

This is a super simple modelling process, but I think it’s a good illustration — if you don’t include any data from China when training your computer to recognize names, it’s not going to get any Chinese names right!

Machine learning projects are like any other development projects, with more complex algorithms.  There’s no magic and there’s a lot of perspiration (hopefully figuratively rather than literally) involved in getting a program which behaves correctly.

Related Posts

Reproducibility And ML Projects

Pete Warden explains some of the difficulties around reproducing ML models: Why does this all matter? I’ve had several friends contact me about their struggles reproducing published models as baselines for their own papers. If they can’t get the same accuracy that the original authors did, how can they tell if their new approach is […]

Read More

XGBoost With Python

Fisseha Berhane looked at Extreme Gradient Boosting with R and now covers it in Python: In both R and Python, the default base learners are trees (gbtree) but we can also specify gblinear for linear models and dart for both classification and regression problems. In this post, I will optimize only three of the parameters […]

Read More


May 2016
« Apr Jun »