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modelcard_template.md

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Model Card for <<modelop.storedModel.modelMetaData.name>>

<<modelop.storedModel.modelMetaData.description>>

Model Details

Model Description

  • Model Use Case: <<modelop.deployableModel.associatedModels.[associationRole=MODEL_USE_CASE].associatedModel.storedModel.modelMetaData.name>>

  • Developed by: <<modelop.storedModel.createdBy>>

  • Model type: <<modelop.storedModel.modelMetaData.modelMethodology>> - <<modelop.storedModel.modelMetaData.type>>

  • Model Documentation: <<modelop.storedModel.modelAssets.[assetRole=MODEL_DOCUMENTATION].filename>>

Model Sources

  • Repository: <<modelop.storedModel.modelMetaData.repositoryInfo.repositoryRemote>> branch: <<modelop.storedModel.modelMetaData.repositoryInfo.repositoryBranch>>

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

<<modelop.storedModel.modelAssets.[assetRole=MODEL_TEST_SOURCE].sourceCode>>

Training Details

Training Data

Dataset Card for <<modelop.storedModel.modelAssets.[assetRole=TRAINING_DATA].filename>>

Dataset Sources

  • Repository: <<modelop.storedModel.modelAssets.[assetRole=TRAINING_DATA].fileUrl>>

Training Procedure

Training Hyperparameters

  • Training regime: [More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Dataset Card for <<modelop.storedModel.modelAssets.[assetRole=BASELINE_DATA].filename>>

Dataset Sources

  • Repository: <<modelop.storedModel.modelAssets.[assetRole=BASELINE_DATA].fileUrl>>

Factors

[More Information Needed]

Metrics

Category Passes Reason
Performance <<modelop.modelTestResult.dmnRuleResults.[testCategory=Performance].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Performance].reason>>
Data Drift - Kolmogorov Smirnov <<modelop.modelTestResult.dmnRuleResults.[testCategory=KS Data Drift].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=KS Data Drift].reason>>
Concept Drift - Kolmogorov Smirnov <<modelop.modelTestResult.dmnRuleResults.[testCategory=KS Concept Drift].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=KS Concept Drift].reason>>
Characteristic Stability <<modelop.modelTestResult.dmnRuleResults.[testCategory=Characteristic Stability].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Characteristic Stability].reason>>
Bias Disparity <<modelop.modelTestResult.dmnRuleResults.[testCategory=Bias Disparity].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Bias Disparity].reason>>
Autocorrelation <<modelop.modelTestResult.dmnRuleResults.[testCategory=Autocorrelation].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Autocorrelation].reason>>
Homoscedacticity: Breusch Pagan <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Breusch Pagan].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Breusch Pagan].reason>>
Homoscedacticity: Engle Lagrange Multiplier <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Engle Lagrange Multiplier].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Engle Lagrange Multiplier].reason>>
Homoscedacticity: Ljung Box Q <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Ljung Box Q].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Homoscedacticity: Ljung Box Q].reason>>
Normality: Kolmogorov Smirnov <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Kolmogorov Smirnov].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Kolmogorov Smirnov].reason>>
Normality: Anderson Darling <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Anderson Darling].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Anderson Darling].reason>>
Normality: Cramer Von Mises <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Cramer Von Mises].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Normality: Cramer Von Mises].reason>>
Linearity: Pearson Correlation <<modelop.modelTestResult.dmnRuleResults.[testCategory=Linearity: Pearson Correlation].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Linearity: Pearson Correlation].reason>>
Mulitcolinearity <<modelop.modelTestResult.dmnRuleResults.[testCategory=Mulitcolinearity].passes>> <<modelop.modelTestResult.dmnRuleResults.[testCategory=Mulitcolinearity].reason>>
  • Performance Metrics:

|<<modelop.modelTestResult.testResults.(performance)[0].values>>|

  • Stability Analysis:
    <<modelopgraph.stability.*>>

Results

[More Information Needed]

Summary

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Model Card Contact

<<modelop.storedModel.createdBy>>