Automated machine learning

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Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand.

Video Automated machine learning

Targets of automation

Automated machine learning can target various stages of the machine learning process:

• Automated data preparation and ingestion (from raw data and miscellaneous formats)
  • Automated column type detection; e.g., boolean, discrete numerical, continuous numerical, or text
  • Automated column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature
  • Automated task detection; e.g., binary classification, regression, clustering, or ranking
• Automated feature engineering
  • Feature selection
  • Feature extraction
  • Meta learning and transfer learning
  • Detection and handling of skewed data and/or missing values
• Automated model selection
• Hyperparameter optimization of the learning algorithm and featurization
• Automated pipeline selection under time, memory, and complexity constraints
• Automated selection of evaluation metics / validation procedures
• Automated problem checking
  • Leakage detection
  • Misconfiguration detection
• Automated analysis of results obtained
• User interfaces and visualizations for automated machine learning

Maps Automated machine learning

Examples

Software tackling various stages of AutoML:

Hyperparameter optimization and model selection

• H2O AutoML provides automated data preparation, hyperparameter tuning via random search, and stacked ensembles in a distributed machine learning platform.
• mlr is a R package that contains several hyperparameter optimization techniques for machine learning problems.

Full pipeline optimization

• Auto-WEKA is a Bayesian hyperparameter optimization layer on top of WEKA.
• auto-sklearn is a Bayesian hyperparameter optimization layer on top of scikit-learn.
• TPOT is a Python library that automatically creates and optimizes full machine learning pipelines using genetic programming.

Deep neural network architecture search

• devol is a Python package that performs Deep Neural Network architecture search using genetic programming.
• Google AutoML for deep learning model architecture selection.

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See also

• Hyperparameter optimization
• Model selection
• Neuroevolution
• Self-tuning

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References

Source of the article : Wikipedia