in ,

LALE: A Python Library Simplifying Automated Machine Learning

LALE: A Python Library Simplifying Automated Machine Learning

Whenever any data scientist thinks of developing a pipeline, they try bringing automated machine learning into the picture to make the task easier. However, due to inconsistent syntax and limited support for advanced features like topology search or higher-order operators, the development becomes tedious. Introducing a solution to this, IBM Research, USA has published a research paper on ‘LALE’: high-level Python interfaces’ library, which simplifies automated machine learning.

The research tends to overcome the following shortcomings of previous research on the inconsistency of Auto-ML libraries:

  1. There is inconsistency in pipeline specification syntax across the manual and automated spectrum.
  2. User needs to learn different syntax to rewrite the code while switching between various Auto-ML tools.
  3. Previous tools do not optimize the topology of the pipeline.
  4. Invalid configuration while combining different hyperparameters
https://github.com/IBM/lale/blob/master/talks/2019-1105-lale.pdf

Characteristics of LALE:

  • LALE helps in selecting algorithms and tune hyperparameters of pipelines, compatible with scikit-learn.
  • LALE provides a highly consistent interface to existing tools such as Hyperopt, SMAC, and GridSearchCV for automation.
  • LALE uses JSON schema for checking correctness.
  • LALE has an expanding library of estimators and transformers for interoperability.
  • LALE uses Python subclassing to implement lifecycle states

Users can install LALE just like any other Python package and edit it with off-the-shelf Python tools such as Jupyter notebooks.

Source: https://arxiv.org/pdf/2007.01977.pdf

Github: https://github.com/ibm/lale

Source: www.marktechpost.com

What do you think?

41 points
Upvote Downvote

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Vizy AI camera runs Tensorflow, OpenCV, PyTorch on Raspberry Pi 4 (Crowdfunding)

Vizy AI camera runs Tensorflow, OpenCV, PyTorch on Raspberry Pi 4 (Crowdfunding)

Google Maps uses AI to better predict when there’s a massive traffic jam

Google Maps uses AI to better predict when there’s a massive traffic jam