| Description | |
| Is there a cat in your dat? | |
| A common task in machine learning pipelines is encoding categorical variables for a given algorithm in a format that allows as much useful signal as possible to be captured. | |
| Because this is such a common task and important skill to master, we've put together a dataset that contains only categorical features, and includes: | |
| - binary features | |
| - low- and high-cardinality nominal features | |
| - low- and high-cardinality ordinal features | |
| - (potentially) cyclical features | |
| This Playground competition will give you the opportunity to try different encoding schemes for different algorithms to compare how they perform. We encourage you to share what you find with the community. | |
| If you're not sure how to get started, you can check out the Categorical Variables section of Kaggle's Intermediate Machine Learning course. | |
| Have Fun! | |
| Evaluation | |
| Submissions are evaluated on area under the ROC curve between the predicted probability and the observed target. | |
| Submission File | |
| For each id in the test set, you must predict a probability for the target variable. The file should contain a header and have the following format: | |
| ``` | |
| id,target | |
| 300000,0.5 | |
| 300001,0.5 | |
| 300002,0.5 | |
| ... | |
| ``` | |
| Dataset Description | |
| In this competition, you will be predicting the probability [0, 1] of a binary target column. The data contains binary features (`bin_*`), nominal features (`nom_*`), ordinal features (`ord_*`), as well as (potentially cyclical) day (of the week) and month features. The string ordinal features `ord_{3-5}` are lexically ordered according to `string.ascii_letters`. | |
| Since the purpose of this competition is to explore various encoding strategies, the data has been simplified in that (1) there are no missing values, and (2) the test set does not contain any unseen feature values. (Of course, in real-world settings both of these factors are often important to consider!) | |
| Files | |
| - `train.csv` - the training set | |
| - `test.csv` - the test set; you must make predictions against this data | |
| - `sample_submission.csv` - a sample submission file in the correct format |