domino logo
Tech Ecosystem
Get started with Python
Step 0: Orient yourself to DominoStep 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get started with R
Step 0: Orient yourself to Domino (R Tutorial)Step 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get Started with MATLAB
Step 1: Orient yourself to DominoStep 2: Create a Domino ProjectStep 3: Configure Your Domino ProjectStep 4: Start a MATLAB WorkspaceStep 5: Fetch and Save Your DataStep 6: Develop Your ModelStep 7: Clean Up Your Workspace
Step 8: Deploy Your Model
Scheduled JobsLaunchers
Step 9: Working with Domino Datasets
Domino Reference
Projects
Projects Overview
Revert Projects and Files
Revert a ProjectRevert a File
Projects PortfolioReference ProjectsProject Goals in Domino 4+
Git Integration
Git Repositories in DominoGit-based Projects with CodeSyncWorking from a Commit ID in Git
Jira Integration in DominoUpload Files to Domino using your BrowserCopy ProjectsFork and Merge ProjectsSearchSharing and CollaborationCommentsDomino Service FilesystemCompare File RevisionsArchive a Project
Advanced Project Settings
Project DependenciesProject TagsRename a ProjectSet up your Project to Ignore FilesUpload files larger than 550MBExporting Files as a Python or R PackageTransfer Project Ownership
Domino Runs
JobsDiagnostic Statistics with dominostats.jsonNotificationsResultsRun Comparison
Advanced Options for Domino Runs
Run StatesDomino Environment VariablesEnvironment Variables for Secure Credential StorageUse Apache Airflow with Domino
Scheduled Jobs
Domino Workspaces
WorkspacesUse Git in Your WorkspaceUse Visual Studio Code in Domino WorkspacesPersist RStudio PreferencesAccess Multiple Hosted Applications in one Workspace Session
Spark on Domino
On-Demand Spark
On-Demand Spark OverviewValidated Spark VersionConfigure PrerequisitesWork with your ClusterManage DependenciesWork with Data
External Hadoop and Spark
Hadoop and Spark OverviewConnect to a Cloudera CDH5 cluster from DominoConnect to a Hortonworks cluster from DominoConnect to a MapR cluster from DominoConnect to an Amazon EMR cluster from DominoRun Local Spark on a Domino ExecutorUse PySpark in Jupyter WorkspacesKerberos Authentication
On-Demand Ray
On-Demand Ray OverviewValidated Ray VersionConfigure PrerequisitesWork with your ClusterManage DependenciesWork with Data
On-Demand Dask
On-Demand Dask OverviewValidated Dask VersionConfigure PrerequisitesWork with Your ClusterManage DependenciesWork with Data
Customize the Domino Software Environment
Environment ManagementDomino Standard EnvironmentsInstall Packages and DependenciesAdd Workspace IDEsAdding Jupyter Kernels
Partner Environments for Domino
Use MATLAB as a WorkspaceUse Stata as a WorkspaceUse SAS as a WorkspaceNVIDIA NGC Containers
Advanced Options for Domino Software Environment
Install Custom Packages in Domino with Git IntegrationAdd Custom DNS Servers to Your Domino EnvironmentConfigure a Compute Environment to User Private Cran/Conda/PyPi MirrorsUse TensorBoard in Jupyter Workspaces
Publish your Work
Publish a Model API
Model Publishing OverviewModel Invocation SettingsModel Access and CollaborationModel Deployment ConfigurationPromote Projects to ProductionExport Model Image
Publish a Web Application
App Publishing OverviewGet Started with DashGet Started with ShinyGet Started with FlaskContent Security Policies for Web Apps
Advanced Web Application Settings in Domino
App Scaling and PerformanceHost HTML Pages from DominoHow to Get the Domino Username of an App Viewer
Launchers
Launchers OverviewAdvanced Launcher Editor
Assets Portfolio Overview
Model Monitoring
Model Monitoring APIsAccessing The Model MonitorGet Started with Model MonitoringModel Monitor DeploymentIngest Data into The Model MonitorModel RegistrationMonitoring Data DriftMonitoring Model QualitySetting Scheduled Checks for the ModelConfigure Notification Channels for the ModelUse Model Monitoring APIsProduct Settings
Connect to your Data
Data in Domino
Datasets OverviewDatasets Best Practices
Data Sources Overview
Connect to Data Sources
External Data Volumes
Work with Data Best Practices
Work with Big Data in DominoWork with Lots of FilesMove Data Over a Network
Advanced User Configuration Settings
User API KeysDomino TokenOrganizations Overview
Use the Domino Command Line Interface (CLI)
Install the Domino Command Line (CLI)Domino CLI ReferenceDownload Files with the CLIForce-Restore a Local ProjectMove a Project Between Domino DeploymentsUse the Domino CLI Behind a Proxy
Browser Support
Get Help with Domino
Additional ResourcesGet Domino VersionContact Domino Technical SupportSupport Bundles
domino logo
About Domino
Domino Data LabKnowledge BaseData Science BlogTraining
User Guide
>
Domino Reference
>
Model Monitoring
>
Monitoring Model Quality

Monitoring Model Quality

If you have a process for generating Ground Truth Labels for your predictions, you can ingest those labels into the Model Monitor to measure and monitor model prediction quality. The monitor uses the row identifiers in the prediction and ground truth data to match the predicted and expected value. Based on those matches, it calculates the different model quality metrics.

Note

For correct ingestion of Ground Truth Label, it is important to map the ground truth label column from the Ground Truth file to its corresponding prediction data column in the prediction dataset. When using the Guided Flow, Step 2 will require you to declare this mapping.

When you apply a date filter, the Model Monitor will use the timestamp values in the prediction data to filter with. It then matches the filtered predictions with the ground truth labels ingested (matches only labels ingested in last 90 days) and calculates the metrics for the matched predictions.

Following prediction quality metrics are supported for Classification models:

  1. Accuracy

  2. Precision

  3. Recall

  4. F1

  5. AUC ROC

  6. Log Loss

  7. Gini (Normalized)

You can view the Confusion Matrix and Classification Report for the data in the selected time range in the Charts section below the metrics table.

Note 1: Model monitoring uses the ‘Weighted' method of calculating these metrics.

Note 2: AUC ROC, Log Loss, Gini Norm are calculated only if Prediction Probability column type is declared as part of schema.

Note 3: Sample Weights is used in calculations for only the Gini Norm metric.

Following prediction quality metrics are supported for Regression models:

  1. Mean Square Error (MSE)

  2. Mean Absolute Error (MAE)

  3. Mean Absolute Percentage Error (MAPE)

  4. R-Squared (R2)

  5. Gini (Normalized)

Ground Truth Config

The Ground Truth Config JSON should capture all information needed to register ground truth data for a registered model. A sample Ground Truth Config is shown below.

{
    "variables": [
        {
            "name": "y_gt",
            "variableType": "ground_truth",
            "valueType": "categorical",
            "forPredictionOutput": "y"
        }
    ],
    "datasetDetails": {
        "name": "BMAF-GTLabels-Webinar.csv",
        "datasetType": "file",
        "datasetConfig": {
            "path": "BMAF-GTLabels-Webinar.csv",
            "fileFormat": "csv"
        },
        "datasourceName": "monitoring-shared-bucket",
        "datasourceType": "s3"
    }
}

Details on each field in the JSON can be found here.

Domino Data LabKnowledge BaseData Science BlogTraining
Copyright © 2022 Domino Data Lab. All rights reserved.