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PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more.
The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. PyCaret was inspired by the caret library in R programming language.
PyCaret is tested and supported on 64-bit systems with:
- Python 3.7, 3.8, 3.9, and 3.10
- Ubuntu 16.04 or later
- Windows 7 or later
You can install PyCaret with Python's pip package manager:
# install pycaretpipinstallpycaretPyCaret's default installation will not install all the optional dependencies automatically. Depending on the use case, you may be interested in one or more extras:
# install analysis extraspipinstallpycaret[analysis]
# models extraspipinstallpycaret[models]
# install tuner extraspipinstallpycaret[tuner]
# install mlops extraspipinstallpycaret[mlops]
# install parallel extraspipinstallpycaret[parallel]
# install test extraspipinstallpycaret[test]
### install multiple extras togetherpipinstallpycaret[analysis,models]Check out all optional dependencies. If you want to install everything including all the optional dependencies:
# install full versionpipinstallpycaret[full]Install the development version of the library directly from the source. The API may be unstable. It is not recommended for production use.
pipinstallgit+https://github.com/pycaret/pycaret.git@master--upgradeDocker creates virtual environments with containers that keep a PyCaret installation separate from the rest of the system. PyCaret docker comes pre-installed with a Jupyter notebook. It can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are always tested for the latest major releases.
# default versiondockerrun-p8888:8888pycaret/slim# full versiondockerrun-p8888:8888pycaret/full# Classification Functional API Example# loading sample datasetfrompycaret.datasetsimportget_datadata=get_data('juice')
# init setupfrompycaret.classificationimport*s=setup(data, target='Purchase', session_id=123)
# model training and selectionbest=compare_models()
# evaluate trained modelevaluate_model(best)
# predict on hold-out/test setpred_holdout=predict_model(best)
# predict on new datanew_data=data.copy().drop('Purchase', axis=1)
predictions=predict_model(best, data=new_data)
# save modelsave_model(best, 'best_pipeline')# Classification OOP API Example# loading sample datasetfrompycaret.datasetsimportget_datadata=get_data('juice')
# init setupfrompycaret.classificationimportClassificationExperiments=ClassificationExperiment()
s.setup(data, target='Purchase', session_id=123)
# model training and selectionbest=s.compare_models()
# evaluate trained models.evaluate_model(best)
# predict on hold-out/test setpred_holdout=s.predict_model(best)
# predict on new datanew_data=data.copy().drop('Purchase', axis=1)
predictions=s.predict_model(best, data=new_data)
# save models.save_model(best, 'best_pipeline')| Functional API | OOP API |
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PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is:
- Experienced Data Scientists who want to increase productivity.
- Citizen Data Scientists who prefer a low code machine learning solution.
- Data Science Professionals who want to build rapid prototypes.
- Data Science and Machine Learning students and enthusiasts.
To train models on the GPU, simply pass use_gpu = True in the setup function. There is no change in the use of the API; however, in some cases, additional libraries have to be installed. The following models can be trained on GPUs:
- Extreme Gradient Boosting
- CatBoost
- Light Gradient Boosting Machine requires GPU installation
- Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15
You can apply Intel optimizations for machine learning algorithms and speed up your workflow. To train models with Intel optimizations use sklearnex engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:
pipinstallscikit-learn-intelexPyCaret is completely free and open-source and licensed under the MIT license.
| Important Links | Description |
|---|---|
| ⭐ Tutorials | Tutorials developed and maintained by core developers |
| 📋 Example Notebooks | Example notebooks created by community |
| 📙 Blog | Official blog by creator of PyCaret |
| 📚 Documentation | API docs |
| 📺 Videos | Video resources |
| Community Cheat sheet | |
| 📢 Discussions | Community Discussion board on GitHub |
| 🛠️ Release Notes | Release Notes |