{"id":13934746,"url":"https://github.com/catboost/tutorials","last_synced_at":"2025-05-14T14:07:54.212Z","repository":{"id":39987672,"uuid":"138584767","full_name":"catboost/tutorials","owner":"catboost","description":"CatBoost tutorials repository","archived":false,"fork":false,"pushed_at":"2025-03-30T17:32:29.000Z","size":48271,"stargazers_count":1062,"open_issues_count":6,"forks_count":412,"subscribers_count":36,"default_branch":"master","last_synced_at":"2025-04-12T13:57:33.710Z","etag":null,"topics":["catboost","ipython","ipython-notebook","kaggle","titanic-dataset","tutorials"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/catboost.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-06-25T11:18:45.000Z","updated_at":"2025-04-11T03:37:33.000Z","dependencies_parsed_at":"2023-01-19T20:18:36.478Z","dependency_job_id":"44e3ce0e-c3a5-4a96-a064-68896e1d4533","html_url":"https://github.com/catboost/tutorials","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/catboost%2Ftutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/catboost%2Ftutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/catboost%2Ftutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/catboost%2Ftutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/catboost","download_url":"https://codeload.github.com/catboost/tutorials/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254159194,"owners_count":22024558,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["catboost","ipython","ipython-notebook","kaggle","titanic-dataset","tutorials"],"created_at":"2024-08-07T23:01:12.587Z","updated_at":"2025-05-14T14:07:49.204Z","avatar_url":"https://github.com/catboost.png","language":"Jupyter Notebook","readme":"# CatBoost tutorials\n\n## Basic\n\nIt's better to start CatBoost exploring from this basic tutorials.\n\n### Python\n\n* [Python Tutorial](python_tutorial.ipynb)\n    * This tutorial shows some base cases of using CatBoost, such as model training, cross-validation and predicting, as well as some useful features like early stopping,  snapshot support, feature importances and parameters tuning.\n* [Python Tutorial with task](python_tutorial_with_tasks.ipynb)\n    * There are 17 questions in this tutorial. Try answering all of them, this will help you to learn how to use the library.\n\n### R\n\n* [R Tutorial](r_tutorial.ipynb)\n    * This tutorial shows how to convert your data to CatBoost Pool, how to train a model and how to make cross validation and parameter tunning.\n\n### Command line\n\n* [Command Line Tutorial](cmdline_tutorial/cmdline_tutorial.md)\n    * This tutorial shows how to train and apply model with the command line tool.\n\n## Classification\n\n* [Classification Tutorial](classification/classification_tutorial.ipynb)\n    * Here is an example for CatBoost to solve binary classification and multi-classification problems.\n\n## Ranking\n* [Ranking Tutorial](ranking/ranking_tutorial.ipynb)\n    * CatBoost is learning to rank on Microsoft dataset (msrank).\n\n## Feature selection\n* [Feature selection Tutorial](feature_selection/eval_tutorial.ipynb)\n    * This tutorial shows how to make feature evaluation with CatBoost and explore learning rate.\n\n## Model analysis\n\n* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb)\n    * This tutorial shows how to evaluate importances of the train objects for test objects, and how to detect broken train objects by using the importance scores.\n\n* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb)\n    * This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances.\n\n* [Export CatBoost Model in JSON format Tutorial](model_analysis/model_export_as_json_tutorial.ipynb)\n    * This tutorial shows how to save catboost model in JSON format and apply it.\n\n* [Visualization of CatBoost decision trees tutorial](model_analysis/visualize_decision_trees_tutorial.ipynb)\n    * This tutorial shows how to visualize catboost decision trees.\n\n* [Feature statistics tutorial](model_analysis/feature_statistics_tutorial.ipynb)\n    * This tutorial shows how to calculate feature statistics for catboost model.\n\n* [CatBoost PredictionDiff Feature Importance Tutorial](./prediction_diff_feature_importance_tutorial.ipynb)\n    * This tutorials shows how to use PredictionDiff feature importances.\n\n## Custom loss\n\n* [Custom Metrics Tutorial](custom_loss/custom_metric_tutorial.md)\n    * This tutorial shows how to add custom per-object metrics.\n\n## Apply model\n\n* [CatBoost CoreML Tutorial](apply_model/coreml/coreml_export_tutorial.ipynb)\n    * Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device.\n\n* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md)\n    * Catboost model could be saved as standalone C++ code.\n\n* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md)\n    * Catboost model could be saved as standalone Python code.\n\n* [Apply CatBoost model from Java](apply_model/java/train_model.ipynb)\n    * Explore how to apply CatBoost model from Java application. If you just want to look at code snippets you can go directly to [CatBoost4jPredictionTutorial.java](apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java)\n\n* [Apply CatBoost model from Rust](apply_model/rust/train_model.ipynb)\n    * Explore how to apply CatBoost model from Rust application. If you just want to look at code snippets you can go directly to [main.rs](apply_model/rust/src/main.rs)\n\n* [Convert LightGBM to CatBoost to use CatBoost fast appliers](apply_model/fast_light_gbm_applier.ipynb)\n    * Convert LightGBM to CatBoost, save resulting CatBoost model and use CatBoost C++, Python, C# or other applier, which in case of not symmetric trees will be around 7-10 faster than native LightGBM one.\n    * Note that CatBoost applier with CatBoost models is even faster, because it uses specific fast symmetric trees.\n\n## Tools\n\n* [Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_tutorial.ipynb)\n    * This is a basic tutorial which shows how to run gradient boosting on CPU and GPU on Google Colaboratory.\n\n* [Regression on Gradient Boosting: CPU vs GPU](tools/google_colaboratory_cpu_vs_gpu_regression_tutorial.ipynb)\n    * This is a basic tutorial which shows how to run regression on gradient boosting on CPU and GPU on Google Colaboratory.\n\n## Competition examples\n\n* [Kaggle Paribas Competition Tutorial](competition_examples/kaggle_paribas.ipynb)\n    * This tutorial shows how to get to a 9th place on Kaggle Paribas competition with only few lines of code and training a CatBoost model.\n\n* [ML Boot Camp V Competition Tutorial](competition_examples/mlbootcamp_v_tutorial.ipynb)\n    * This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost.\n\n* [CatBoost \u0026 TensorFlow Tutorial](competition_examples/quora_w2v.ipynb)\n    * This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data.\n\n## Events\n\n* [PyData Moscow tutorial](events/pydata_moscow_oct_13_2018.ipynb)\n    * Tutorial from PyData Moscow, October 13, 2018.\n\n* [PyData NYC tutorial](events/pydata_nyc_oct_19_2018.ipynb)\n    * Tutorial from PyData New York, October 19, 2018.\n\n* [PyData LA tutorial](events/pydata_la_oct_21_2018.ipynb)\n    * Tutorial from PyData Los Angeles, October 21, 2018.\n\n* [PyData Moscow tutorial](events/datastart_moscow_apr_27_2019.ipynb)\n    * Tutorial from PyData Moscow, April 27, 2019.\n\n* [PyData London tutorial](events/2019_pydata_london/pydata_london_2019.ipynb)\n    * Tutorial from PyData London, June 15, 2019.\n\n* [PyData Boston tutorial](events/2019_odsc_east/odsc_east_2019.ipynb)\n    * Tutorial from PyData Boston, April 30, 2019.\n\n## Tutorials in Russian\n\n* Find tutorials in Russian on the separate [page](ru/README.md).\n","funding_links":[],"categories":["Jupyter Notebook","梯度提升和树模型","Implementations","Boosting"],"sub_categories":["CatBoost"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcatboost%2Ftutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcatboost%2Ftutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcatboost%2Ftutorials/lists"}