PyCaret releases new version 2.3.6

The new features include eda, dashboard. convert_model, check_fairness, create_api. create_docker. create_app, and optimize_threshold.

Moez Ali, founder and author of PyCaret, has announced a new version of PyCaret. The version 2.3.6. is its biggest release so far in terms of new features, Ali said. PyCaret is an open-source, low-code machine learning library in Python designed to automate machine learning workflows. 

The new features include eda, dashboard. convert_model, check_fairness, create_api. create_docker. create_app, and optimize_threshold.

The 2.3.6 version also has made logging level configurable by an environment variable, made the optional path in AWS configurable, fixed TSNE plot with PCA, fixed rendering of streamlit plots, fixed class names in tree plot and fixed NearZeroVariance preprocessor.

The company has also launched new documentation which Ali dubbed The Bible of PyCaret covering all the information, from installation to deployment in one place.

The document is divided into 7 parts detailing information on installation, quickstart, tutorials, modules, preprocessing, functions and release notes. It also covers requirements of dependences, GPU, environment and docker. Additionally, the document has tutorials for regression, NLP, Time series, clustering and classification. 

PyCaret is a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray etc.

PyCaret is ideal for data scientists looking to increase productivity, Citizen Data Scientists who prefer low code machine learning solutions, Data Science Professionals who want to build rapid prototypes etc.

More Great AIM Stories

Meeta Ramnani
Meeta’s interest lies in finding out real practical applications of technology. At AIM, she writes stories that question the new inventions and the need to develop them. She believes that technology has and will continue to change the world very fast and that it is no more ‘cool’ to be ‘old-school’. If people don’t update themselves with the technology, they will surely be left behind.

More Stories

OUR UPCOMING EVENTS

8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

MORE FROM AIM
Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Vijaysinh Lendave
How to Evaluate Recommender Systems with RGRecSys?

A recommender system, sometimes known as a recommendation engine, is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. In this post, we will look at RGRecSys, a library that performs constraint evaluation of recommender systems.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM