| Description | |
| Get started on this competition through Kaggle Scripts. | |
| The Otto Group is one of the world’s biggest e-commerce companies, with subsidiaries in more than 20 countries, including Crate & Barrel (USA), Otto.de (Germany), and 3 Suisses (France). We are selling millions of products worldwide every day, with several thousand products being added to our product line. | |
| A consistent analysis of the performance of our products is crucial. However, due to our diverse global infrastructure, many identical products get classified differently. Therefore, the quality of our product analysis depends heavily on the ability to accurately cluster similar products. The better the classification, the more insights we can generate about our product range. | |
| For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. The winning models will be open-sourced. | |
| Evaluation | |
| Submissions are evaluated using the multi-class logarithmic loss. Each product has been labeled with one true category. For each product, you must submit a set of predicted probabilities (one for every category). The formula is then: | |
| \[ \text{log loss} = -\frac{1}{N} \sum_{i=1}^N \sum_{j=1}^M y_{ij} \log (p_{ij}), \] | |
| where \( N \) is the number of products in the test set, \( M \) is the number of class labels, \( \log \) is the natural logarithm, \( y_{ij} \) is 1 if observation \( i \) is in class \( j \) and 0 otherwise, and \( p_{ij} \) is the predicted probability that observation \( i \) belongs to class \( j \). | |
| The submitted probabilities for a given product are not required to sum to one because they are rescaled prior to being scored (each row is divided by the row sum). In order to avoid the extremes of the log function, predicted probabilities are replaced with \( \max(\min(p,1-10^{-15}),10^{-15}) \). | |
| Submission Format | |
| You must submit a csv file with the product id, all candidate class names, and a probability for each class. The order of the rows does not matter. The file must have a header and should look like the following: | |
| ``` | |
| id,Class_1,Class_2,Class_3,Class_4,Class_5,Class_6,Class_7,Class_8,Class_9 | |
| 1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 | |
| 2,0.0,0.2,0.3,0.3,0.0,0.0,0.1,0.1,0.0 | |
| ... | |
| ``` | |
| Dataset Description | |
| See, fork, and run a random forest benchmark model through Kaggle Scripts. | |
| Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further. | |
| There are nine categories for all products. Each target category represents one of our most important product categories (like fashion, electronics, etc.). The products for the training and testing sets are selected randomly. | |
| File Descriptions: | |
| - `trainData.csv` - the training set | |
| - `testData.csv` - the test set | |
| - `sampleSubmission.csv` - a sample submission file in the correct format | |
| Data Fields: | |
| - `id` - an anonymous id unique to a product | |
| - `feat_1, feat_2, ..., feat_93` - the various features of a product | |
| - `target` - the class of a product |