| Description | |
| Synthetically-Generated Datasets | |
| 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! | |
| Evaluation | |
| Submissions are evaluated on micro-averaged F1-Score between predicted and actual values. | |
| Submission File | |
| For each id in the test set, you must predict the corresponding outcome. The file should contain a header and have the following format: | |
| id,outcome | |
| 1235,lived | |
| 1236,lived | |
| 1237,died | |
| etc. | |
| Dataset Description | |
| The dataset for this competition (both train and test) was generated from a deep learning model trained on a portion of the Horse Survival 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. Good luck! | |
| Files | |
| train.csv - the training dataset; outcome is the (categorical) target | |
| test.csv - the test dataset; your objective is to predict outcome | |
| sample_submission.csv - a sample submission file in the correct format |