Package: pycaret
An open-source, low-code machine learning library in Python🎉🎉🎉PyCaret 3.3 is now available. 🎉🎉🎉pip install --upgrade pycaretDocs•Tutorials•Blog•LinkedIn•YouTube•SlackOverviewCI/CDCodeDownloadsLicenseCommunityWelcome to PyCaretPyCaret 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.🚀 Installation🌐 Option 1: Install via PyPiPyCaret is tested and supported on 64-bit systems with:Python 3.9, 3.10 and 3.11Ubuntu 16.04 or laterWindows 7 or laterYou 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 alloptional dependencies. If you want to install everything including all the optional dependencies:# install full versionpipinstallpycaret[full]📄 Option 2: Build from SourceInstall 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--upgrade📦 Option 3: DockerDocker 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🏃‍♂️ Quickstart1. Functional API# 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')2. OOP API# 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')📁 ModulesClassificationFunctional APIOOP APIRegressionFunctional APIOOP APITime SeriesFunctional APIOOP APIClusteringFunctional APIOOP APIAnomaly DetectionFunctional APIOOP API👥 Who should use PyCaret?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.🎮 Training on GPUsTo 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 BoostingCatBoostLight Gradient Boosting Machine requiresGPU installationLogistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requirescuML >= 0.15🖥️ PyCaret Intel sklearnex supportYou can applyIntel optimizationsfor machine learning algorithms and speed up your workflow. To train models with Intel optimizations usesklearnexengine. There is no change in the use of the API, however, installation of Intel sklearnex is required:pipinstallscikit-learn-intelex🤝 Contributors📝 LicensePyCaret is completely free and open-source and licensed under theMITlicense.ℹ️ More InformationImportant LinksDescription:star:TutorialsTutorials developed and maintained by core developers:clipboard:Example NotebooksExample notebooks created by community:orange_book:BlogOfficial blog by creator of PyCaret:books:DocumentationAPI docs:tv:VideosVideo resources✈️Cheat sheetCommunity Cheat sheet:loudspeaker:DiscussionsCommunity Discussion board on GitHub:hammer_and_wrench:Release NotesRelease Notes