As of May 31, 2023, we have updated our Code of Conduct.

Questions tagged [kaggle]

Pertaining to questions arising from competitions hosted on Use this tag only for topics SPECIFIC to Kaggle, not just because your data come from Kaggle.

Filter by
Sorted by
Tagged with
0 votes
0 answers

Should I train my final model on the (train+validation) set before final submission?

I was trying to evaluate different classification models on MNIST dataset. There are two datasets provided : train - 42000 images, and ...
Dietzsche Nostoevsky's user avatar
0 votes
1 answer

Can this approach be used for machine learning using train-test split?

So let's say I have a dataset with 1000 samples, 20 cols. Regression problem. I use train-test split, say 80-20% I create a Model, lets say Random Forest. I use gridsearchCV to find the best model ...
Sharan Shetty's user avatar
2 votes
0 answers

What have we learned from time series competitions (or Kaggle)? [closed]

What are the main takeaways we, as statistics community, learned from (time series) competitions? Kaggle, or the M.. competitions, seem such a valuable source, but the (only) main insight I remember ...
Arne Jonas Warnke's user avatar
1 vote
1 answer

What are the best practices for selecting your cross validation strategy? [closed]

I am new to Kaggle competitions and want to know if their are best practices for selecting a robust CV.
Kurtis Pykes's user avatar
3 votes
2 answers

How to make train/test split with given class weights

I am doing simple multi class classification ML problem. I was given train data with perfectly balanced classes. However the data I must predict is not balanced. I was able to deduct the class ...
Dmitry Petrov's user avatar
1 vote
1 answer

Cross validation best practice for competition purpose

I'm fairly new to DS scene and I have been learning about theories and doing practices on kaggle/participate in private competition. For real world problems, my understanding is that you split out ...
bchoiNY's user avatar
  • 13
1 vote
0 answers

What do they mean by Robust Cross-Validation?

I was reading a Kaggler Interview article and they kept specifying the importance of a stable and good cross-validation in order to win their competitions. What do they mean by that? I usually just ...
Chipmunkafy's user avatar
60 votes
7 answers

Industry vs Kaggle challenges. Is collecting more observations and having access to more variables more important than fancy modelling?

I'd hope the title is self explanatory. In Kaggle, most winners use stacking with sometimes hundreds of base models, to squeeze a few extra % of MSE, accuracy... In general, in your experience, how ...
Tom's user avatar
  • 1,284
1 vote
1 answer

SVC doing great on validation & test data but scored very low on predicted data

First of all, this is my first machine learning project after taking Andrew Ng's course, so please bear with me. I'm working on the most famous dataset, the Titanic data. First, I split the dataset ...
Blaze Tama's user avatar
14 votes
2 answers

Are Kaggle competitions just won by chance?

Kaggle competitions determine final rankings based on a held-out test set. A held-out test set is a sample; it may not be representative of the population being modeled. Since each submission is ...
user0's user avatar
  • 5,550