Tag Archive: feature engineering


Hello my AI friends!

Today, I would like to share with you skrobot!

skrobot is an open-source Python module I have created at MedoidAI for automating Machine Learning (ML) tasks. It is built on top of scikit-learn framework and follows object-oriented design (OOD) for ML task creation, execution, and reproducibility. Multiple ML tasks can be combined together to implement an experiment. It also provides seamless tracking and logging of experiments (e.g. saving experiments’ parameters).

It can help Data Scientists and Machine Learning Engineers:

  • to keep track of modelling experiments / tasks
  • to automate the repetitive (and boring) stuff when designing ML modelling pipelines
  • to spend more time on the things that truly matter when solving a problem

The current release of skrobot (1.0.13) supports the following ML tasks for binary classification problems:

  • Automated feature engineering / synthesis
  • Feature selection
  • Model selection (i.e. hyperparameters search)
  • Model evaluation
  • Model training
  • Model prediction

For more information you can check out the online documentation!

Lastly, many thanks to all contributors who helped to extend and support skrobot.

Stay safe!

Despite the promise of Feature Learning in Deep Learning -where dense, low-dimensional and compressed representations can be learned automatically from high-dimensional raw data- usually Feature Engineering is the most important factor for the success of an ML project.

Among ML practitioners the best learning algorithms and models are well-known and most effort is done to transform the data in order to express as much as possible the useful parts that model best the underlying problem.

In other words, the success of an ML project depends mostly on the data representation and not model selection / tuning. When the features are not garbage usually even the simplest algorithms with default hyperparameter values can give good results.