Starting from version 1.0.0 our open-source Automated Machine Learning Python package mljar-supervised is supporting fairness aware training of Machine Learning pipelines. Our AutoML can measure fairness and mitigate bias for provided sensitive features. We support three Machine Learning tasks: binary classification, multiclass classification and regression. We provide usage and implementation details in this article.

Automated Machine Learning (AutoML) is a process of building a complete Machine Learning pipeline automatically, without (or with minimal) human help. The AutoML solutions are quite new, with the first research papers from 2013 (Auto-Weka), 2015 (Auto-sklearn), and 2016 (TPOT). Currently, there are several AutoML open-source frameworks and commercial platforms available that can work with a variety of data. There is worth mentioning such open-source solutions like AutoGluon, H2O, or MLJAR AutoML.

The MLJAR provides an open-source Automated Machine Learning framework for creating Machine Learning pipelines. It has a built-in heuristic algorithm for hyperparameters tuning based on: random search over a defined set of hyperparameters values, and hill-climbing over best solutions to search for further improvements. This solution works very well on Machine Learning tasks under a selected time budget. However, there might be situations when the model performance is the primary goal and the time needed for computation is not the limit. Thus, we propose the new mode: “Optuna” in the MLJAR framework. In this mode, we utilize the Optuna hyperparameters tuning framework. It is availbale in the mljar-supervised package starting from version 0.10.0.

The AutoML stands for Automated Machine Learning. It builds a Machine Learning pipeline in an automated way. But how exactly it works? What is behind the scene? There are many proprietary AutoML systems, and we probably never get to know how they work. Luckily, the MLJAR AutoML is open-source. Its code is available at GitHub. In this article, we will look inside MLJAR AutoML to show how it works.

Python Notebooks provide interactive computing environment that is perfect for experimenting with data. The Notebooks are widely used by Data Scientists in data analysis and discovery tasks. Currently, there are many versions of Notebooks. The first, and the most used version is Jupyter Notebook. There are also many cloud-based Notebooks, like Kaggle Notebooks or CoCalc Notebooks.

If there has been an open-source library that has made me an avid machine learning practitioner and won the battle of the AutoMLs hands down it has to be MlJar. I simply can’t stop eulogizing this library because it has helped overcome my deficiency in the field of coding and programming but at the same time automating the predictive modeling flow with very little user involvement. I have taken it for a spin in a few Hackathons and am not overtly surprised to find it amongst the top performers. It saves a lot of time as you do not need Data Preprocessing and feature Engineering before feeding the dataset to the model.

Automated Machine Learning is the end-to-end process of applying machine learning in an automatic way.

Random Forest versus AutoML you say. Hmmm…, it’s obvious that the performance of AutoML will be better. You will check many models and then ensemble them. This is true, but I would like to show you other advantages of AutoML, that will help you deal with dirty, real-life data.

Sometimes we don’t even realize how common machine learning (ML) is in our daily lives. Various “intelligent” algorithms help us for instance with finding the most important facts (Google), they suggest what movie to watch (Netflix), or influence our shopping decisions (Amazon). The biggest international companies quickly recognized the potential of machine learning and transferred it to business solutions.