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        "description": "# kaggle-metrics <img src=\"https://raw.githubusercontent.com/krzjoa/kaggle-metrics/master/img/kmlogo.png\" align=\"right\" width = \"120px\"/>\n![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg) \n[![PyPI version](https://badge.fury.io/py/kaggle-metrics.svg)](https://badge.fury.io/py/kaggle-metrics ) \n[![Documentation Status](https://readthedocs.org/projects/kaggle-metrics/badge/?version=latest)](https://kaggle-metrics.readthedocs.io/en/latest/?badge=latest) \n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) \n\nMetrics for Kaggle competitions.\n\n**See wiki**: [Implemented metrics](https://github.com/krzjoa/kaggle-metrics/wiki/Implemented-metrics)\n\n## Installation\n```bash\npython3.7 -m pip install git+https://github.com/krzjoa/kaggle-metrics.git\n```\nor:\n\n```bash\npython3.7 -m pip install kaggle_metrics\n```\n\n## Usage\n```python\nfrom xgboost import XGBRegressor\nimport kaggle_metrics as km\n\nX_train, y_train, X_test, y_test = get_data()\n\n# Train\nclf = XGBRegressor()\nclf.fit(X_train, y_train)\n\n# Get predictions\ny_pred = clf.predict(X_test)\n\n# Evaluate with kaggle-metrics\nkm.rmse(y_test, y_pred)\n\n\n```\n\n\n\n\n\nDocumentation\n-------------\nThe full documentation is at http://kaggle-metrics.rtfd.org.\n\n\n\n",
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