Instructions to use Sagargupta1610/leetcode-rating-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Sagargupta1610/leetcode-rating-predictor with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Sagargupta1610/leetcode-rating-predictor") - Notebooks
- Google Colab
- Kaggle
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Download backtest.md from Sagargupta1610/leetcode-rating-predictor: direct link, hf CLI and curl.
- Browser
- Download file 857 Bytes
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https://huggingface.co/Sagargupta1610/leetcode-rating-predictor/resolve/main/backtest.md
- Command line
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hf download hf://Sagargupta1610/leetcode-rating-predictor/backtest.md
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curl -L -o backtest.md https://huggingface.co/Sagargupta1610/leetcode-rating-predictor/resolve/main/backtest.md
857 Bytes
Backtest
Generated by python scripts/backtest.py on 2026-10-19. Each prediction
uses only the history a user had before that contest, built by the same feature
code the API serves, and is compared with the rating change LeetCode applied.
| Metric | Value |
|---|---|
| Contests scored | 173 |
| Users | 50 |
| MAE | 9.11 |
| RMSE | 19.85 |
| Median absolute error | 5.59 |
| Predictions within 10 points | 73.4% |
| Direction right (gain vs loss) | 90.8% |
| MAE of always predicting 0 | 29.34 |
Users were sampled with seed 7 from data/usernames.json past index
12000, the slice scripts/update_data.py never fetches, so none of
these contests is in the training data. Up to the last 5 rated contests
per user count, if they started within 365 days of the run.
869 sampled accounts had no contest in that window.
Model: models/weights.npz.