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Evaluating a specific model configuration without running a full automated search.
Distinct from Machine Learning Evaluation: Focuses on the manual fitting of one configuration instead of a general comparative analysis.
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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
Evaluates a specific model and hyperparameter set without executing a full automated search.