models classification accuracy eval

classification-accuracy-eval

Overview

The "Classification Accuracy Evaluation" is a model designed to assess the effectiveness of a data classification system. It involves matching each prediction against the ground truth, subsequently assigning a "Correct" or "Incorrect" score. The cumulative results are then leveraged to generate performance metrics, such as accuracy, providing an overall measure of the system's proficiency in data classification.

Inference samples

Inference type	CLI	VS Code Extension
Real time	deploy-promptflow-model-cli-example	deploy-promptflow-model-vscode-extension-example
Batch	N/A	N/A

Sample inputs and outputs (for real-time inference)

Sample input

{
    "inputs": {
        "groundtruth": "App",
        "prediction": "App"
    }
}

Sample output

{
    "outputs": {
        "grade": "Correct"
    }
}

Version: 6

View in Studio: https://ml.azure.com/registries/azureml/models/classification-accuracy-eval/version/6

Properties

is-promptflow: True

azureml.promptflow.section: gallery

azureml.promptflow.type: evaluate

azureml.promptflow.name: Classification Accuracy Evaluation

azureml.promptflow.description: Measuring the performance of a classification system by comparing its outputs to groundtruth.

inference-min-sku-spec: 2|0|14|28

inference-recommended-sku: Standard_DS3_v2