Automated machine learning
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|Machine learning and|
Automated machine learning (AutoML) is the process of automating end-to-end the process of applying machine learning to real-world problems. In a typical machine learning application, practitioners have a dataset consisting of input data points to train on. The raw data itself may not be in a form that all algorithms may be applicable to it out of the box. An expert may have to 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 process of applying machine learning end-to-end offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand. However, AutoML is not a silver bullet and can introduce additional parameters of its own, called hyperhyperparameters, which may need some expertise to be set themselves. But it does make application of Machine Learning easier for non-experts.
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
- Automated model selection
- Hyperparameter optimization of the learning algorithm and featurization
- Automated pipeline selection under time, memory, and complexity constraints
- Automated selection of evaluation metrics / validation procedures
- Automated problem checking
- Leakage detection
- Misconfiguration detection
- Automated analysis of results obtained
- User interfaces and visualizations for automated machine learning
Notable platforms tackling various stages of AutoML:
Hyperparameter optimization and model selection
- 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.
- ATM is an open source software library under the Human Data Interaction project (HDI) at MIT. It is a distributed, scalable AutoML system designed with ease of use in mind.
- H2O AutoML provides automated data preparation, hyperparameter tuning via random search, and stacked ensembles in a distributed machine learning platform.
Full pipeline optimization
- TPOT is a Python library that automatically creates and optimizes full machine learning pipelines using genetic programming.
- H2O Driverless AI is an automated machine learning platform developed by H2O.ai for automated visualization, feature engineering, model training, hyperparameter optimization, and explainability.
- dotData Enterprise is an automated machine learning platform developed by dotData for automated feature engineering, model training, hyperparameter optimization explainability and operationalization of AI and ML models.
- TransmogrifAI is a Scala/SparkML library created by Salesforce for automated data cleansing, feature engineering, model selection, and hyperparameter optimization
- RECIPE  is a framework based on grammar-based genetic programming that builds customized scikit-learn classification pipelines.
- GA-Auto-MLC and Auto-MEKAGGP are freely-available methods that perform automated multi-label classification on the MEKA software.
- ML-Plan is an open-source AutoML tool based on Hierarchical Task Network planning, which is implemented to work with WEKA and scikit-learn algorithms. Furthermore it has been extended to support an unlimited number of preprocessing steps using scikit-learn and for the problem domain of multi-label classification based on MEKA.
Deep neural network architecture search
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Once TransmogrifAI has extracted features from the dataset, it’s primed to begin automated model training. At this stage, it runs a cadre of machine learning algorithms in parallel on the data, automatically selects the best-performing model, and samples and recalibrates predictions to avoid imbalanced data.
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