| Evaluation | |
| Submissions will be evaluated using Mean Absolute Error (MAE), where each \(x_i\) represents the predicted target, \(y_i\) represents the ground truth, and \(n\) is the number of rows in the test set. | |
| Submission File | |
| For each id in the test set, you must predict the target Age. The file should contain a header and have the following format: | |
| ``` | |
| id,yield | |
| 74051,10.2 | |
| 74051,3.6 | |
| 74051,11.9 | |
| ``` | |
| etc. | |
| Dataset Description | |
| The dataset for this competition (both train and test) was generated from a deep learning model trained on the Crab Age 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: You can use this notebook to generate additional synthetic data for this competition if you would like. | |
| Files | |
| - `train.csv` - the training dataset; Age is the target | |
| - `test.csv` - the test dataset; your objective is to predict the probability of Age (the ground truth is int but you can predict int or float) | |
| - `sample_submission.csv` - a sample submission file in the correct format |