January 14, 2022

"To evaluate thousands of machine learning models, learn about AutoML libraries."

Automated Machine Learning (AutoML) is a technique that assists in automating several critical components of the machine learning pipeline. This machine learning pipeline entails several steps, including data exploration, data engineering, feature engineering, model training, hyperparameter tuning, and model monitoring.

Each component of an end-to-end machine learning project varies according to the project. To automate the machine learning pipeline, data scientists use AutoML frameworks.

Advantages of AutoML:

• Increase efficiency by automating routine tasks. AutoML enables data scientists to devote more time to problems rather than models.
• Additionally, automated machine learning pipelines assist in avoiding potential errors caused by manual work.
• Develop a model, conduct stratified cross-validation, and assess classification metrics.
• Automatically tune a classification model's hyper-parameters
• Analyze the model's performance with the help of various plots.
• Make predictions based on new / previously unknown data.
• Make a model available for future use by saving/loading it.

Let's look at some of the most popular AutoML libraries for machine learning projects.

PyCaret

PyCaret is a low-code machine learning library written in Python that aims to shorten the time required to go from hypothesis to insight. It is well-suited for experienced data scientists looking to boost the productivity of their machine learning experiments by incorporating PyCaret into their workflows.

Auto-Sklearn

Auto-SKLearn is a scikit-learn based automated machine learning software package. The advantage of Auto-SKLearn is that it relieves the user of machine learning algorithm selection and hyper-parameter tuning. Moreover, It includes methods for feature engineering such as One-Hot, digital feature standardization, and principal component analysis (PCA). The model processes classification and regression problems using SKLearn estimators. With Auto-sklearn 2.0, this AutoML for machine learning project leverages Bayesian optimization and meta-learning.

TPOT

TPOT is a tree-based pipeline optimization tool that uses genetic algorithms to optimize machine learning pipelines. TPOT is based on scikit-learn and includes its regressor and classifier. TPOT searches through thousands of possible channels and selects the data that best fits the data.

H₂O

Developed by H₂O.ai, H₂O is an in-memory distributed machine learning platform. It is open-source and distributed. H₂O is compatible with both R and Python. It is consistent with the most widely used statistical and machine learning algorithms, such as gradient boosted machines, generalized linear models, and deep learning.

Auto-Keras

DATA Lab developed Auto-Keras, an open-source software library for automated machine learning (AutoML). Auto-Keras provides functions for automatically determining the architecture and hyperparameters of deep learning models.

MLBox

MLBox is a robust Python library for Automated Machine Learning. According to the official document, it includes features such as fast data reading and distributed data reprocessing/cleansing/formatting, highly robust feature selection and leak detection, and precise hyper-parameter optimization and prediction with model interpretation.

AutoGluon

AWS has open-sourced AutoGluon, an autoML framework developed for deep learning workloads. Unlike other autoML libraries, it supports image classification, object detection, text, and real-world applications spanning images.

HyperOpt-Sklearn

HyperOpt-Sklearn is an open-source Python library for Bayesian optimization that wraps the HyperOpt library. HyperOpt is a Python library for optimizing models with many hyperparameters on a large scale. The HyperOpt library is well-suited for large-scale models due to its optimization procedure being scalable across multiple cores. It optimizes the machine learning pipeline, including preprocessing data, selecting models, and tuning hyperparameters.

Conclusion

When data scientists use AutoML, they can implement machine learning much more efficiently. AutoML libraries can assist data scientists by automating hyperparameter tuning and model selection.

Image source: Unsplash

Dr Nivash Jeevanandam PhD,
Researcher | Senior Technology Journalist