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Generation of commands to run automated ML searches across multiple datasets.
Distinct from Model Configuration Settings: Generates execution commands for batch runs rather than adjusting operational settings for a single model.
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This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur
Creates execution commands to run automated machine learning across multiple datasets with specific time budgets.