# Classifier Clone

*English translation by SOTA – AI Community of the Hungarian original. Licensed CC BY-NC-SA 4.0, like the original. Organisers who would like this translation removed can email sota.ai.community@gmail.com.*

**Magyar MI Diákolimpia** (Hungarian AI Olympiad)\
**Classifier Clone** (Klasszifikáló Klón)\
Task description\
24 May 2025

## 1. Classifier Clone

You are given a pretrained neural network, which we trained for a classification task on a specific **training** dataset. To solve the task, you have at your disposal:

- the trained neural network;
- the training dataset (`X_train`) and labels (`y_train`);
- a small subset of the test dataset (`X_train_small`) and labels (`y_train_small`).

The goal is to build a machine learning model that is **not based on a neural network**. The job of this model is **to approximate the output of the trained neural network as closely as possible on the full test dataset**, that is, to try to imitate its behaviour.

## 2. Scoring

The final model will be evaluated on the full test dataset. Scoring is based on the accuracy metric, according to the following scale (the right end of each interval is open):

- 0 - 0.5: 0 points
- 0.5 - 0.62: 20 points
- 0.62 - 0.66: 30 points
- 0.66 - 0.68: 40 points
- 0.68 - 0.70: 50 points
- 0.70 - 0.71: 70 points
- 0.71 - 0.728: 80 points
- 0.728 - 0.80: 90 points
- 0.80 - 1: 100 points

**Maximum score:** 100 points\
During scoring, the seed set in the first cell will be used. In addition, it is recommended to set a random state for the models as well, since this can affect the results they achieve. During evaluation, all code will be run only once.

## 3. Technical information

To solve the task, the attached `.ipynb` notebook file, the weights of the secret model and the dataset are sufficient. For evaluation, a file named `train-test.pt` is also provided, but you may use it only at the end of your solution, to evaluate the model. It is **forbidden** to use it during training or development. To help you, several useful libraries and guides can be found at the beginning of the notebook.

After solving the task, regardless of whether you worked in Google Colab or locally, you must upload the `.ipynb` file to the CMS system for the corresponding task. No other file needs to be uploaded.

## 4. Useful resources

- 🔗 [Scikit-Learn – Documentation](https://scikit-learn.org/stable/)
- 🔗 [Catboost – Documentation](https://catboost.ai/docs/en/concepts/tutorials)
- 🔗 [LightGBM – Documentation](https://lightgbm.readthedocs.io/en/stable/)
- 🔗 [XGBoost – Documentation](https://machinelearningmastery.com/calculate-bleu-score-for-text-python/)