Evaluate AutoML classification and regression models

This page shows you how to evaluate your AutoML classification and regression models.

Vertex AI provides model evaluation metrics to help you determine the performance of your models, such as precision and recall metrics. Vertex AI calculates evaluation metrics by using the test set.

Before you begin

Before you can evaluate your model, you must train it.

How you use model evaluation metrics

Model evaluation metrics provide quantitative measurements of how your model performed on the test set. How you interpret and use those metrics depends on your business need and the problem your model is trained to solve. For example, you might have a lower tolerance for false positives than for false negatives or the other way around. These kinds of questions affect which metrics you would focus on.

Getting evaluation metrics

You can get an aggregate set of evaluation metrics for your model and, for some objectives, evaluation metrics for a particular class or label. Evaluation metrics for a particular class or label is also known as an evaluation slice. The following content describes how to get aggregate evaluation metrics and evaluation slices by using the Google Cloud console or API.

Google Cloud console

  1. In the Google Cloud console, in the Vertex AI section, go to the Models page.

    Go to the Models page

  2. In the Region drop-down, select the region where your model is located.

  3. From the list of models, click your model, which opens the model's Evaluate tab.

    In the Evaluate tab, you can view your model's aggregate evaluation metrics, such as the Average precision and Recall.

    If the model objective has evaluation slices, the console shows a list of labels. You can click a label to view evaluation metrics for that label, as shown in the following example:

    label selection in console

API

API requests for getting evaluation metrics is the same for each data type and objective, but the outputs are different. The following samples show the same request but different responses.

Getting aggregate model evaluation metrics

The aggregate model evaluation metrics provide information about the model as a whole. To see information about a specific slice, list the model evaluation slices.

To view aggregate model evaluation metrics, use the projects.locations.models.evaluations.get method.

Select the tab below for your objective:

Classification

Vertex AI returns an array of confidence metrics. Each element shows evaluation metrics at a different confidenceThreshold value (starting from 0 and going up to 1). By viewing different threshold values, you can see how the threshold affects other metrics such as precision and recall.

Select a tab that corresponds to your language or environment:

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Region where your model is stored.
  • PROJECT: Your project ID.
  • MODEL_ID: The ID of the model resource.
  • PROJECT_NUMBER: Your project's automatically generated project number.
  • EVALUATION_ID: ID for the model evaluation (appears in the response).

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


import com.google.cloud.aiplatform.v1.ModelEvaluation;
import com.google.cloud.aiplatform.v1.ModelEvaluationName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import java.io.IOException;

public class GetModelEvaluationTabularClassificationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // To obtain evaluationId run the code block below after setting modelServiceSettings.
    //
    // try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings))
    // {
    //   String location = "us-central1";
    //   ModelName modelFullId = ModelName.of(project, location, modelId);
    //   ListModelEvaluationsRequest modelEvaluationsrequest =
    //   ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();
    //   for (ModelEvaluation modelEvaluation :
    //     modelServiceClient.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {
    //       System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
    //   }
    // }
    String project = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String evaluationId = "YOUR_EVALUATION_ID";
    getModelEvaluationTabularClassification(project, modelId, evaluationId);
  }

  static void getModelEvaluationTabularClassification(
      String project, String modelId, String evaluationId) throws IOException {
    ModelServiceSettings modelServiceSettings =
        ModelServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
      String location = "us-central1";
      ModelEvaluationName modelEvaluationName =
          ModelEvaluationName.of(project, location, modelId, evaluationId);
      ModelEvaluation modelEvaluation = modelServiceClient.getModelEvaluation(modelEvaluationName);

      System.out.println("Get Model Evaluation Tabular Classification Response");
      System.out.format("\tName: %s\n", modelEvaluation.getName());
      System.out.format("\tMetrics Schema Uri: %s\n", modelEvaluation.getMetricsSchemaUri());
      System.out.format("\tMetrics: %s\n", modelEvaluation.getMetrics());
      System.out.format("\tCreate Time: %s\n", modelEvaluation.getCreateTime());
      System.out.format("\tSlice Dimensions: %s\n", modelEvaluation.getSliceDimensionsList());
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * TODO(developer): Uncomment these variables before running the sample
 * (not necessary if passing values as arguments). To obtain evaluationId,
 * instantiate the client and run the following the commands.
 */
// const parentName = `projects/${project}/locations/${location}/models/${modelId}`;
// const evalRequest = {
//   parent: parentName
// };
// const [evalResponse] = await modelServiceClient.listModelEvaluations(evalRequest);
// console.log(evalResponse);

// const modelId = 'YOUR_MODEL_ID';
// const evaluationId = 'YOUR_EVALUATION_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);

async function getModelEvaluationTabularClassification() {
  // Configure the parent resources
  const name = `projects/${project}/locations/${location}/models/${modelId}/evaluations/${evaluationId}`;
  const request = {
    name,
  };

  // Get model evaluation request
  const [response] = await modelServiceClient.getModelEvaluation(request);

  console.log('Get model evaluation tabular classification response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tMetrics schema uri : ${response.metricsSchemaUri}`);
  console.log(`\tMetrics : ${JSON.stringify(response.metrics)}`);
  console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tSlice dimensions : ${response.sliceDimensions}`);

  const modelExplanation = response.modelExplanation;
  console.log('\tModel explanation');
  if (!modelExplanation) {
    console.log('\t\t{}');
  } else {
    const meanAttributions = modelExplanation.meanAttributions;
    if (!meanAttributions) {
      console.log('\t\t\t []');
    } else {
      for (const meanAttribution of meanAttributions) {
        console.log('\t\tMean attribution');
        console.log(
          `\t\t\tBaseline output value : \
            ${meanAttribution.baselineOutputValue}`
        );
        console.log(
          `\t\t\tInstance output value : \
            ${meanAttribution.instanceOutputValue}`
        );
        console.log(
          `\t\t\tFeature attributions : \
            ${JSON.stringify(meanAttribution.featureAttributions)}`
        );
        console.log(`\t\t\tOutput index : ${meanAttribution.outputIndex}`);
        console.log(
          `\t\t\tOutput display name : \
            ${meanAttribution.outputDisplayName}`
        );
        console.log(
          `\t\t\tApproximation error : \
            ${meanAttribution.approximationError}`
        );
      }
    }
  }
}
getModelEvaluationTabularClassification();

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

from google.cloud import aiplatform


def get_model_evaluation_tabular_classification_sample(
    project: str,
    model_id: str,
    evaluation_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    """
    To obtain evaluation_id run the following commands where LOCATION
    is the region where the model is stored, PROJECT is the project ID,
    and MODEL_ID is the ID of your model.

    model_client = aiplatform.gapic.ModelServiceClient(
        client_options={
            'api_endpoint':'LOCATION-aiplatform.googleapis.com'
            }
        )
    evaluations = model_client.list_model_evaluations(parent='projects/PROJECT/locations/LOCATION/models/MODEL_ID')
    print("evaluations:", evaluations)
    """
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.ModelServiceClient(client_options=client_options)
    name = client.model_evaluation_path(
        project=project, location=location, model=model_id, evaluation=evaluation_id
    )
    response = client.get_model_evaluation(name=name)
    print("response:", response)

Regression

Select a tab that corresponds to your language or environment:

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Region where your model is stored.
  • PROJECT: Your project ID.
  • MODEL_ID: The ID of the model resource.
  • PROJECT_NUMBER: Your project's automatically generated project number.
  • EVALUATION_ID: ID for the model evaluation (appears in the response).

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


import com.google.cloud.aiplatform.v1.ModelEvaluation;
import com.google.cloud.aiplatform.v1.ModelEvaluationName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import java.io.IOException;

public class GetModelEvaluationTabularRegressionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // To obtain evaluationId run the code block below after setting modelServiceSettings.
    //
    // try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings))
    // {
    //   String location = "us-central1";
    //   ModelName modelFullId = ModelName.of(project, location, modelId);
    //   ListModelEvaluationsRequest modelEvaluationsrequest =
    //   ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();
    //   for (ModelEvaluation modelEvaluation :
    //     modelServiceClient.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {
    //       System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
    //   }
    // }
    String project = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String evaluationId = "YOUR_EVALUATION_ID";
    getModelEvaluationTabularRegression(project, modelId, evaluationId);
  }

  static void getModelEvaluationTabularRegression(
      String project, String modelId, String evaluationId) throws IOException {
    ModelServiceSettings modelServiceSettings =
        ModelServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
      String location = "us-central1";
      ModelEvaluationName modelEvaluationName =
          ModelEvaluationName.of(project, location, modelId, evaluationId);
      ModelEvaluation modelEvaluation = modelServiceClient.getModelEvaluation(modelEvaluationName);

      System.out.println("Get Model Evaluation Tabular Regression Response");
      System.out.format("\tName: %s\n", modelEvaluation.getName());
      System.out.format("\tMetrics Schema Uri: %s\n", modelEvaluation.getMetricsSchemaUri());
      System.out.format("\tMetrics: %s\n", modelEvaluation.getMetrics());
      System.out.format("\tCreate Time: %s\n", modelEvaluation.getCreateTime());
      System.out.format("\tSlice Dimensions: %s\n", modelEvaluation.getSliceDimensionsList());
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * TODO(developer): Uncomment these variables before running the sample
 * (not necessary if passing values as arguments). To obtain evaluationId,
 * instantiate the client and run the following the commands.
 */
// const parentName = `projects/${project}/locations/${location}/models/${modelId}`;
// const evalRequest = {
//   parent: parentName
// };
// const [evalResponse] = await modelServiceClient.listModelEvaluations(evalRequest);
// console.log(evalResponse);

// const modelId = 'YOUR_MODEL_ID';
// const evaluationId = 'YOUR_EVALUATION_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);

async function getModelEvaluationTabularRegression() {
  // Configure the parent resources
  const name = `projects/${project}/locations/${location}/models/${modelId}/evaluations/${evaluationId}`;
  const request = {
    name,
  };

  // Get model evaluation request
  const [response] = await modelServiceClient.getModelEvaluation(request);

  console.log('Get model evaluation tabular regression response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tMetrics schema uri : ${response.metricsSchemaUri}`);
  console.log(`\tMetrics : ${JSON.stringify(response.metrics)}`);
  console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tSlice dimensions : ${response.sliceDimensions}`);

  const modelExplanation = response.modelExplanation;
  console.log('\tModel explanation');
  if (!modelExplanation) {
    console.log('\t\t{}');
  } else {
    const meanAttributions = modelExplanation.meanAttributions;
    if (!meanAttributions) {
      console.log('\t\t\t []');
    } else {
      for (const meanAttribution of meanAttributions) {
        console.log('\t\tMean attribution');
        console.log(
          `\t\t\tBaseline output value : \
            ${meanAttribution.baselineOutputValue}`
        );
        console.log(
          `\t\t\tInstance output value : \
            ${meanAttribution.instanceOutputValue}`
        );
        console.log(
          `\t\t\tFeature attributions : \
            ${JSON.stringify(meanAttribution.featureAttributions)}`
        );
        console.log(`\t\t\tOutput index : ${meanAttribution.outputIndex}`);
        console.log(
          `\t\t\tOutput display name : \
            ${meanAttribution.outputDisplayName}`
        );
        console.log(
          `\t\t\tApproximation error : \
            ${meanAttribution.approximationError}`
        );
      }
    }
  }
}
getModelEvaluationTabularRegression();

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

from google.cloud import aiplatform


def get_model_evaluation_tabular_regression_sample(
    project: str,
    model_id: str,
    evaluation_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    """
    To obtain evaluation_id run the following commands where LOCATION
    is the region where the model is stored, PROJECT is the project ID,
    and MODEL_ID is the ID of your model.

    model_client = aiplatform.gapic.ModelServiceClient(
        client_options={
            'api_endpoint':'LOCATION-aiplatform.googleapis.com'
            }
        )
    evaluations = model_client.list_model_evaluations(parent='projects/PROJECT/locations/LOCATION/models/MODEL_ID')
    print("evaluations:", evaluations)
    """
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.ModelServiceClient(client_options=client_options)
    name = client.model_evaluation_path(
        project=project, location=location, model=model_id, evaluation=evaluation_id
    )
    response = client.get_model_evaluation(name=name)
    print("response:", response)

List all evaluation slices (classification models only)

The projects.locations.models.evaluations.slices.list method lists all evaluation slices for your model. You must have the model's evaluation ID, which you can get when you view the aggregated evaluation metrics.

You can use model evaluation slices to determine how the model performed on a specific label. The value field tells you which label the metrics are for.

Vertex AI returns an array of confidence metrics. Each element shows evaluation metrics at a different confidenceThreshold value (starting from 0 and going up to 1). By viewing different threshold values, you can see how the threshold affects other metrics such as precision and recall.

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Region where Model is located. For example, us-central1.
  • PROJECT: Your project ID.
  • MODEL_ID: The ID of your model.
  • EVALUATION_ID: ID of the model evaluation that contains the evaluation slices to list.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations/EVALUATION_ID/slices

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations/EVALUATION_ID/slices"

PowerShell

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models/MODEL_ID/evaluations/EVALUATION_ID/slices" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.