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# Tutorials | ||
# CatBoost tutorials | ||
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## Python tutorials | ||
## Basic | ||
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* Main CatBoost tutorial with base features demonstration: | ||
* [Python Tutorial](catboost_python_tutorial.ipynb) | ||
* 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. | ||
It's better to start CatBoost exploring from this basic tutorials. | ||
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* CatBoost model analysis tutorials: | ||
* [Object Importance Tutorial](advanced_tutorials/catboost_object_importance_tutorial.ipynb) | ||
* This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects. | ||
### Python | ||
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* [SHAP Values Tutorial](advanced_tutorials/shap_values_tutorial.ipynb) | ||
* This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances. | ||
* [Python Tutorial](python_tutorial.ipynb) | ||
* 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. | ||
* [Python Tutorial with task](python_tutorial_with_tasks.ipynb) | ||
* There are 17 questions in this tutorial. Try answering all of them, this will help you to learn how to use the library. | ||
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* CatBoost performance at different competitions: | ||
* [Kaggle Paribas Tutorial](advanced_tutorials/kaggle_paribas.ipynb) | ||
* This tutorial shows how to get to a 9th place on paribas competition with only few lines of code and training a CatBoost model. | ||
### R | ||
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* [ML Boot Camp Tutorial](advanced_tutorials/mlbootcamp_v_tutorial.ipynb) | ||
* This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost. | ||
* [R Tutorial](r_tutorial.ipynb) | ||
* 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. | ||
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* CatBoost and TensorFlow: | ||
* [CatBoost & TensorFlow Tutorial](advanced_tutorials/quora_catboost_w2v.ipynb) | ||
* This tutorial shows how to use CatBoost together with TensorFlow if you have text as input data. | ||
### Command line | ||
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* CatBoost and CoreML: | ||
* [CatBoost CoreML Tutorial](advanced_tutorials/catboost_coreml_export_tutorial.ipynb) | ||
* This tutorial shows how to convert CatBoost model to CoreML format and use it on an iPhone. | ||
* [Command Line Tutorial](cmdline_tutorial.md) | ||
* This tutorial shows how to train and apply model with the command line tool. | ||
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## R tutorials | ||
## Classification | ||
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* Main CatBoost tutorial with base features demonstration: | ||
* [R Tutorial](catboost_r_tutorial.ipynb) | ||
* 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. | ||
* [Classification Tutorial](classification/classification_tutorial.ipynb) | ||
* Here is an example for CatBoost to solve binary classification and multi-classification problems. | ||
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## Command line tutorials | ||
## Ranking | ||
* [Ranking Tutorial](ranking/ranking_tutorial.ipynb) | ||
* CatBoost is learning to rank on Microsoft dataset (msrank). | ||
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* Main CatBoost tutorial with base features demonstration: | ||
* [Command Line Tutorial](catboost_cmdline_tutorial.md) | ||
* This tutorial shows how to train and apply model with the command line tool. | ||
## Feature selection | ||
* [Feature selection Tutorial](feature_selection/eval_tutorial.ipynb) | ||
* This tutorial shows how to make feature evaluation with CatBoost and explore learning rate. | ||
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## Custom loss tutorial | ||
## Model analysis | ||
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* Adding custom per-object error function tutorial: | ||
* [Custom Metrics Tutorial](advanced_tutorials/catboost_custom_metric_tutorial.md) | ||
* This tutorial shows how to add custom per-object metrics. | ||
* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb) | ||
* This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects. | ||
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||
* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb) | ||
* This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances. | ||
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## Custom loss | ||
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* [Custom Metrics Tutorial](custom_loss/custom_metric_tutorial.md) | ||
* This tutorial shows how to add custom per-object metrics. | ||
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## Apply model | ||
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* [CatBoost CoreML Tutorial](apply_model/coreml_export_tutorial.ipynb) | ||
* Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device. | ||
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* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md) | ||
* Catboost model could be saved as standalone C++ code. | ||
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* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md) | ||
* Catboost model could be saved as standalone Python code. | ||
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## Competition examples | ||
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* [Kaggle Paribas Competition Tutorial](competition_examples/kaggle_paribas.ipynb) | ||
* 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. | ||
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* [ML Boot Camp V Competition Tutorial](competition_examples/mlbootcamp_v_tutorial.ipynb) | ||
* This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost. | ||
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||
* [CatBoost & TensorFlow Tutorial](competition_examples/quora_w2v.ipynb) | ||
* This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data. | ||
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## Events | ||
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* [PyData NYC tutorial](events/pydata_nyc_oct_19_2018.ipynb) | ||
* Tutorial from PyData New York, October 19, 2018. | ||
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* [PyData LA tutorial](events/pydata_la_oct_21_2018.ipynb) | ||
* Tutorial from PyData Los Angeles, October 21, 2018. | ||
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## Tutorials on Russian | ||
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* Find tutorials on Russian language on the separate [page](ru/README.md). |
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# Apply model | ||
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* [CatBoost CoreML Tutorial](apply_model/coreml_export_tutorial.ipynb) | ||
* Explore this tutorial to learn how to convert CatBoost model to CoreML format and use it on any iOS device. | ||
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* [Export CatBoost Model as C++ code Tutorial](apply_model/model_export_as_cpp_code_tutorial.md) | ||
* Catboost model could be saved as standalone C++ code. | ||
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* [Export CatBoost Model as Python code Tutorial](apply_model/model_export_as_python_code_tutorial.md) | ||
* Catboost model could be saved as standalone Python code. |
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# Classification | ||
|
||
* [Classification Tutorial](classification/classification_tutorial.ipynb) | ||
* Here is an example for CatBoost to solve binary classification and multi-classification problems. |
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# Competition examples | ||
|
||
* [Kaggle Paribas Competition Tutorial](competition_examples/kaggle_paribas.ipynb) | ||
* 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. | ||
|
||
* [ML Boot Camp V Competition Tutorial](competition_examples/mlbootcamp_v_tutorial.ipynb) | ||
* This is an actual 7th place solution by Mikhail Pershin. Solution is very simple and is based on CatBoost. | ||
|
||
* [CatBoost & TensorFlow Tutorial](competition_examples/quora_w2v.ipynb) | ||
* This tutorial shows how to use CatBoost together with TensorFlow on Kaggle Quora Question Pairs competition if you have text as input data. |
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# Custom loss | ||
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* [Custom Metrics Tutorial](custom_loss/custom_metric_tutorial.md) | ||
* This tutorial shows how to add custom per-object metrics. |
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# Feature selection | ||
* [Feature selection Tutorial](feature_selection/eval_tutorial.ipynb) | ||
* This tutorial shows how to make feature evaluation with CatBoost and explore learning rate. |
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# Model analysis | ||
|
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* [Object Importance Tutorial](model_analysis/object_importance_tutorial.ipynb) | ||
* This tutorial shows how to evaluate importances of the train objects for test objects. And with using of importance scores detect noisy train objects. | ||
|
||
* [SHAP Values Tutorial](model_analysis/shap_values_tutorial.ipynb) | ||
* This tutorial shows how to use [SHAP](https://github.com/slundberg/shap) python-package to get and visualize feature importances. |
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# Ranking | ||
* [Ranking Tutorial](ranking/ranking_tutorial.ipynb) | ||
* CatBoost is learning to rank on Microsoft dataset (msrank). |
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# Туториалы CatBoost на русском языке | ||
* [Туториал Kaggle Amazon](kaggle_amazon_tutorial_ru.ipynb) | ||
* Туториал с демонстрацией основного функционала библиотеки на датасете Amazon Employee Access Challenge. | ||
* [ML Session, Новосибирск 2018](ml_session_2018_tutorial_ru.ipynb) | ||
* Туториал c мероприятия [ML Session](https://events.yandex.ru/events/meetings/19-april-2018/) прошедшего 19 Апреля 2018 в Новосибирске. | ||
* [CatBoost и ClickHouse, Москва 2017](catboost_with_clickhouse_tutorial_ru.ipynb) | ||
* Туториал с мероприятия [Опенсорс в Яндексе: CatBoost и ClickHouse](https://events.yandex.ru/events/ClickHouse/30-november-2017/) прошедшего 30 Ноября 2017 в Москве. |
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