AutoML Libraries for Python, you should know.

AutoML provides tools that automatically discover good machine learning model pipelines for a particular dataset without much user intervention. It is ideal for machine learning practitioners or domain experts new to machine learning to get quick and good results for a predictive modeling task. Open-source libraries, such as the scikit-learn machine learning library, are available for using AutoML methods with popular machine learning libraries in Python. In this article, you will get to know about the top open-source Auto ML libraries for Python. The blog first discusses what automated Machine Learning is and its use as a part of machine learning for beginners.


Table of Contents


  • Automated Machine Learning
  • Auto-Sklearn
  • TPOT
  • Auto-Keras
  • Hyperopt-Sklearn
  • Takeaway


If you’re interested in machine learning certification, these four Python libraries are the way to go.


Automated Machine Learning


AutoML or Automated Machine Learning involves selecting machine learning models, selecting data preparation, and modeling hyperparameters for a predictive modeling task. It refers to techniques that allow non-experts and semi-sophisticated machine learning practitioners to quickly discover a promising modeling pipeline for their machine learning tasks. The central approach is defining a massive hierarchical optimization problem that involves identifying machine learning models and data transforms themselves, also the hyperparameters for the models. AutoML is offered as a service by many companies where a dataset is uploaded. A model pipeline can be hosted or downloaded and used via a web service that is MLaaS. The typical examples include service offerings from Microsoft, Amazon, and Google. Moreover, open-source libraries are available to implement AutoML techniques that focus on the specific models, hyperparameters, and data transforms used in the search space and the types of algorithms used to optimize or navigate the search space possibilities, with versions of Bayesian Optimization being the most popular. AutoML has the following advantages:


  1. Automated ML pipelines help avoid potential errors caused by manual work
  2. It improves efficiency by running repetitive tasks automatically. This also data scientists to focus on the problem more than the model.
  3. AutoML is a considerable step towards the democratization of machine learning and allowing everyone to use ML features. 




Auto-Sklearn is an automated machine learning toolkit that integrates with the standard sklearn interface as people in the community are familiar with seamlessly. The use of recent methods like Bayesian Optimization has allowed the library to be built to navigate the domain of possible models and learn to deduce if a specific configuration will work well on a given task or not. Auto-sklearn can be thought of as the best library to get started with AutoML. In addition to discovering model selections and data preparation for a dataset, it learns from models to perform well on similar datasets. The highly performing models are aggregated in an ensemble. Auto-SKLearn creates a pipeline by using Bayes search to optimize that channel. Two components are added for hyperparameter tuning in the ML framework by Bayesian reasoning. Optimizers are initialized using Bayes and Meta-Learning. The evaluation of the auto collection construction of the configuration takes place during the optimization process. Auto-SKLearn performs well for small and medium datasets, but it cannot produce modern deep learning systems for large datasets with the most advanced performance. 




TPOT, or Tree-based Pipeline Optimization tool, is a Python library for automated machine learning. It uses genetic algorithms to optimize machine learning pipelines. TPOT is built on top of scikit-learn and uses its classifier and regressor methods. The tool explores thousands of possible pipelines and finds the best fit for the data. TPOT uses a tree-based structure to represent predictive modeling’s pipeline, including modeling algorithms, model hyperparameters, and data preparation. This tool puts a greater emphasis on data preparation. It automates preprocessing, feature selection, and construction through an evolutionary tree-based structure. TPOT automatically optimizes post designing the machine learning pipelines. 




Auto-Keras is an open-source software library for AutoML developed by DATA Lab. It is built on top of the deep learning network Keras. Auto-Keras provides functions to search for hyper-parameters and architecture for deep learning models automatically. Auto-Keras follow the classic Scikit-Learn API design, and thus it is easy to use. The current version provides the ability to search for hyperparameters during deep learning automatically. The trend is to simplify Machine Learning by using automatic Neural Architecture Search (NAS) algorithms in auto-Keras. NAS employs a set of algorithms that automatically adjust models to replace deep learning practitioners/engineers. 


Deep Learning and neural networks are significantly more powerful and challenging to equip than standard machine learning libraries. With support for image, structured data, text, and interfaces for beginners and those seeking to get more involved in technicalities. The neural architecture search methods used by AutoKeras eliminates the hard work and ambiguity for the user. Although AutoKeras is time-consuming, many user-specified parameters control the running time, such as the search space size and number of models explored.



Developed by James Bergstra, HyperOpt is an open-source Python library for Bayesian optimization. It is designed for large-scale optimizations for models with several parameters. It allows the optimization procedure to be scaled across multiple machines and multiple crores. The HyperOpt Library is wrapped by the HyperOpt-Sklearn and allows the automatic search of machine learning algorithms, data preparation methods, and hyperparameters for regression and classification tasks. HyperOpt is challenging to use directly since it’s very technical and requires optimization parameters and procedures to be carefully specified. Therefore, it is recommended to use HyperOpt-sklearn as it incorporates the HyperOpt and sklearn library. HyperOpt focuses on some hyperparameters that go into specific models, although it does support preprocessing. 



In this article, we saw the top open-source AutoML libraries for Scikit-learn in Python. If you want to understand it in detail, it is essential to take up a machine learning course to learn about the fundamentals. Then a focused machine learning training can be taken up to enter the world of automated Machine Learning.