graph LR
Data_Preparation["Data Preparation"]
Model_Training["Model Training"]
Hyperparameter_Optimization["Hyperparameter Optimization"]
Ensemble_Stacking["Ensemble & Stacking"]
Prediction_Evaluation["Prediction & Evaluation"]
Data_Preparation -- "Provides cleaned and transformed data, including selected features" --> Model_Training
Data_Preparation -- "Provides cleaned and transformed data, including selected features, for training stacking models" --> Ensemble_Stacking
Data_Preparation -- "Provides processed new data for inference" --> Prediction_Evaluation
Hyperparameter_Optimization -- "Supplies optimized parameters to enhance model performance" --> Model_Training
Hyperparameter_Optimization -- "Supplies optimized parameters for ensemble model configuration" --> Ensemble_Stacking
Model_Training -- "Feeds trained base models or their predictions for stacking" --> Ensemble_Stacking
Model_Training -- "Delivers trained models and their predictions for final output" --> Prediction_Evaluation
Ensemble_Stacking -- "Provides trained ensemble models and their predictions for final output" --> Prediction_Evaluation
click Data_Preparation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/MLBox/Data_Preparation.md" "Details"
click Model_Training href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/MLBox/Model_Training.md" "Details"
click Ensemble_Stacking href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/MLBox/Ensemble_Stacking.md" "Details"
click Prediction_Evaluation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/MLBox/Prediction_Evaluation.md" "Details"
The MLBox architecture is designed as a streamlined machine learning pipeline, commencing with the Data Preparation component, which ingests and transforms raw data into a usable format. This prepared data is then channeled to the Model Training component for individual model development and to the Ensemble & Stacking component for advanced model combination. The Hyperparameter Optimization component iteratively refines the parameters for both base and ensemble models, enhancing their predictive power. Ultimately, the Prediction & Evaluation component receives the processed data from Data Preparation and the trained models from Model Training and Ensemble & Stacking to generate final predictions and comprehensive performance reports, completing the end-to-end machine learning workflow.
Data Preparation [Expand]
Responsible for loading, cleaning, transforming, and feature engineering raw datasets.
Related Classes/Methods: None
Model Training [Expand]
Manages the training and configuration of individual base machine learning models.
Related Classes/Methods: None
Tunes hyperparameters for base and ensemble models to achieve optimal performance.
Related Classes/Methods: None
Ensemble & Stacking [Expand]
Implements advanced ensemble techniques to combine predictions from multiple base models.
Related Classes/Methods: None
Prediction & Evaluation [Expand]
Orchestrates the final prediction phase and generates reports or visualizations.
Related Classes/Methods: None