on_boarding.md

graph LR
    Data_Handling_Module["Data Handling Module"]
    Metrics_Module["Metrics Module"]
    Recalibration_Module["Recalibration Module"]
    Visualization_Module["Visualization Module"]
    Core_Utilities_Module["Core Utilities Module"]
    Data_Handling_Module -- "provides raw predictions and true values for" --> Metrics_Module
    Data_Handling_Module -- "supplies raw predictions and true values for" --> Recalibration_Module
    Data_Handling_Module -- "provides initial data for" --> Visualization_Module
    Recalibration_Module -- "outputs adjusted predictions to" --> Metrics_Module
    Metrics_Module -- "supplies calculated metrics for" --> Visualization_Module
    Recalibration_Module -- "provides recalibrated data for" --> Visualization_Module
    Core_Utilities_Module -- "supports" --> Data_Handling_Module
    Core_Utilities_Module -- "supports" --> Metrics_Module
    Core_Utilities_Module -- "supports" --> Recalibration_Module
    Core_Utilities_Module -- "supports" --> Visualization_Module
    click Metrics_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uncertainty-toolbox/Metrics_Module.md" "Details"
    click Recalibration_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uncertainty-toolbox/Recalibration_Module.md" "Details"
    click Visualization_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/uncertainty-toolbox/Visualization_Module.md" "Details"

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Details

The uncertainty-toolbox is architected as a streamlined ML toolkit, primarily focused on the lifecycle of uncertainty quantification: data input, metric calculation, recalibration, and visualization. The Data Handling Module serves as the entry point for all data, feeding raw predictions and true values to the Metrics Module for evaluation and the Recalibration Module for adjustment. The Recalibration Module can then refine these predictions, which can be re-evaluated by the Metrics Module. Finally, both the original/recalibrated data and the computed metrics converge at the Visualization Module to generate comprehensive plots. This flow is consistently supported by the Core Utilities Module, which provides foundational helper functions, ensuring a clear, modular, and extensible design optimized for analyzing and improving uncertainty predictions.

Data Handling Module

Manages the input and output of data, including loading, generating, and basic preprocessing of uncertainty-related datasets.

Related Classes/Methods:

• uncertainty_toolbox.data

Metrics Module [Expand]

The central component for calculating a comprehensive suite of uncertainty quantification metrics, encompassing accuracy, calibration, and scoring rules.

Related Classes/Methods:

• uncertainty_toolbox.metrics
• uncertainty_toolbox.metrics_calibration
• uncertainty_toolbox.metrics_accuracy
• uncertainty_toolbox.metrics_scoring_rule

Recalibration Module [Expand]

Provides various algorithms and methods to adjust or "recalibrate" uncertainty predictions, aiming to improve their reliability and alignment with observed outcomes.

Related Classes/Methods:

• uncertainty_toolbox.recalibration

Visualization Module [Expand]

Dedicated to generating insightful plots and visual representations of uncertainty data, calculated metrics, and the effects of recalibration.

Related Classes/Methods:

• uncertainty_toolbox.viz

Core Utilities Module

Contains fundamental helper functions, mathematical operations, and general utilities leveraged across different modules of the library.

Related Classes/Methods:

• uncertainty_toolbox.utils

FAQ