| Welcome to the 2023 edition of Kaggle's Playground Series! Thank you to everyone who participated in and contributed to Season 3 of the Playground Series so far! With the same goal to give the Kaggle community a variety of fairly light-weight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science, we will continue launching the Tabular Tuesday in April every Tuesday at 00:00 UTC, with each competition running for 2 weeks. Again, these will be fairly light-weight datasets that are synthetically generated from real-world data, and will provide an opportunity to quickly iterate through various models and feature engineering ideas, create visualizations, etc. | |
| Using synthetic data for Playground competitions allows us to strike a balance between having real-world data (with named features) and ensuring test labels are not publicly available. This allows us to host competitions with more interesting datasets than in the past. While there are still challenges with synthetic data generation, the state-of-the-art is much better now than when we started the Tabular Playground Series two years ago, and the goal is to produce datasets that have far fewer artifacts. Please feel free to give us feedback on the datasets for the different competitions so that we can continue to improve! | |
| Submissions will be evaluated based on MPA@3. Each submission can contain up to 3 predictions (all separated by spaces), and the earlier a correct prediction occurs, the higher score it will receive. | |
| For each id in the test set, you must predict the target prognosis. The file should contain a header and have the following format: | |
| id, prognosis | |
| 707, Dengue West_Nile_fever Malaria | |
| 708, Lyme_disease West_Nile_fever Dengue | |
| 709, Dengue West_Nile_fever Lyme_disease | |
| etc. | |
| The dataset for this competition (both train and test) was generated from a deep learning model trained on the Vector Borne Disease Prediction dataset. Feature distributions are close to, but not exactly the same as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance. Note that in the original dataset some prognoses contain spaces, but in the competition dataset spaces have been replaced with underscores to work with the MPA@K metric. | |
| Files | |
| train.csv - the training dataset; prognosis is the target | |
| test.csv - the test dataset; your objective is to predict prognosis | |
| sample_submission.csv - a sample submission file in the correct format |