kaggle

Assume I know nothing about what Kaggle is, what the contest is about, and even what 'overfitting' means.

The contest was valid and well-organized (this could happen on any Kaggle competition with little data).

If you go on kaggle or other data science sites, linear and non-linear optimization don't seem to get much attention, either.

A poorly designed contest on Kaggle had a submission that deliberately overfit.

In the past year I've completed the Data Science Specialization (JHU, online) and competed in some Kaggle competitions (predictive analytics).

I don't have any relationship with Kaggle other than being a semi-active user, but I really dig what they've got going.

Intro: 'The Diabetic Retinopathy challenge on Kaggle has just finished.

It had one of the larger shakeups in Kaggle history.

Couple points: a) I think one of the biggest challenges in a Kaggle competition is getting away from overfitting to the leaderboard.

I have seen some kaggle competitions do image transformations and put the data back into the training set to increase the robustness of the classifier.

If this were a Kaggle contest, and there were a leaderboard on how well people are doing on the sample data, you'd have a perfect score on the leaderboard.

Check out Kaggle machine learning competitions.

On the 4th page towards the bottom, Kaggle says they will host the team's writeup "soon"; this was 26 days ago.

I'll let the other answers stand for explaining Kaggle and their game, and try to answer "overfitting" with an ELI5 example.

Kaggle is a website on which users can submit statistical models/programs/predictions into competitions, often involving rewards and many competitors.

I'm going to start doing exercises on Kaggle.

If I need someone to work on algorithms, big data, analytics, machine learning, unless they are top of the line in Kaggle, or high rank in TopCoder et al, then a degree will be pretty important for me.