Automated machine learning

What is AutoML

• A process of automating the tasks of applying machine learning to real-world problems

Steps

• Data preparation and ingestion (from raw data and miscellaneous formats)
  • Column type detection; e.g., boolean, discrete numerical, continuous numerical, or text
  • Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature
  • Task detection; e.g., binary classification, regression, clustering, or ranking
• Feature engineering
  • Feature selection
  • Feature extraction
  • Meta learning and transfer learning
  • Detection and handling of skewed data and/or missing values
• Model selection
• Hyperparameter optimization of the learning algorithm and featurization
• Pipeline selection under time, memory, and complexity constraints
• Selection of evaluation metrics and validation procedures
• Problem checking
  • Leakage detection
  • Misconfiguration detection
• Analysis of obtained results
• Creating user interfaces and visualizations

Reference

• 4 Python AutoML Libraries Every Data Scientist Should Know</li>
• The Top 10 AutoML Python Packages</li>
• Wiki