[ aws . sagemaker ]

describe-auto-ml-job

Description

Returns information about an Amazon SageMaker AutoML job.

See also: AWS API Documentation

Synopsis

  describe-auto-ml-job
--auto-ml-job-name <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--auto-ml-job-name (string)

Requests information about an AutoML job using its unique name.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination.

--output (string)

The formatting style for command output.

• json

• text

• table

• yaml

• yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

• on

• off

• auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

• base64

• raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

AutoMLJobName -> (string)

Returns the name of the AutoML job.

AutoMLJobArn -> (string)

Returns the ARN of the AutoML job.

InputDataConfig -> (list)

Returns the input data configuration for the AutoML job..

(structure)

A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see .

Note

A validation dataset must contain the same headers as the training dataset.

DataSource -> (structure)

The data source for an AutoML channel.

S3DataSource -> (structure)

The Amazon S3 location of the input data.

S3DataType -> (string)

The data type.

A ManifestFile should have the format shown below:

[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

An S3Prefix should have the following format:

s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

S3Uri -> (string)

The URL to the Amazon S3 data source.

CompressionType -> (string)

You can use Gzip or None . The default value is None .

TargetAttributeName -> (string)

The name of the target variable in supervised learning, usually represented by ‘y’.

ContentType -> (string)

The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

ChannelType -> (string)

The channel type (optional) is an enum string. The default value is training . Channels for training and validation must share the same ContentType and TargetAttributeName . For information on specifying training and validation channel types, see ` How to specify training and validation datasets https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-data-sources-training-or-validation`__ .

OutputDataConfig -> (structure)

Returns the job’s output data config.

KmsKeyId -> (string)

The Key Management Service (KMS) encryption key ID.

S3OutputPath -> (string)

The Amazon S3 output path. Must be 128 characters or less.

RoleArn -> (string)

The Amazon Resource Name (ARN) of the Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

AutoMLJobObjective -> (structure)

Returns the job’s objective.

MetricName -> (string)

The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.

Here are the options:

Accuracy

The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.

AUC

The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.

The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.

AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.

BalancedAccuracy

BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.

F1

The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.

Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.

Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.

F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.

F1macro

The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.

MAE

The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they’re averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.

MSE

The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is

Precision

Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.

PrecisionMacro

The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model’s ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.

R2

R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.

Recall

Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.

Recall is important when testing for cancer because it’s used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive will yield a perfect recall score.

RecallMacro

The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model’s ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive will yield a perfect recall score.

RMSE

Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it’s averaged over all values. It is used in regression analysis to understand model prediction error. It’s an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.

If you do not specify a metric explicitly, the default behavior is to automatically use:

• MSE : for regression.

• F1 : for binary classification

• Accuracy : for multiclass classification.

ProblemType -> (string)

Returns the job’s problem type.

AutoMLJobConfig -> (structure)

Returns the configuration for the AutoML job.

CompletionCriteria -> (structure)

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum runtime, in seconds, an AutoML job has to complete.

If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, will not be completed.

SecurityConfig -> (structure)

The security configuration for traffic encryption or Amazon VPC settings.

VolumeKmsKeyId -> (string)

The key used to encrypt stored data.

EnableInterContainerTrafficEncryption -> (boolean)

Whether to use traffic encryption between the container layers.

VpcConfig -> (structure)

The VPC configuration.

SecurityGroupIds -> (list)

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

(string)

Subnets -> (list)

The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

(string)

DataSplitConfig -> (structure)

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

ValidationFraction -> (float)

The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

CandidateGenerationConfig -> (structure)

The configuration for generating a candidate for an AutoML job (optional).

FeatureSpecificationS3Uri -> (string)

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] } .

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

Note

These column keys may not include the target column.

In ensembling mode, Autopilot will only support the following data types: numeric , categorical , text and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime and sequence .

If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] is case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Mode -> (string)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO will automatically select an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

CreationTime -> (timestamp)

Returns the creation time of the AutoML job.

EndTime -> (timestamp)

Returns the end time of the AutoML job.

LastModifiedTime -> (timestamp)

Returns the job’s last modified time.

FailureReason -> (string)

Returns the failure reason for an AutoML job, when applicable.

PartialFailureReasons -> (list)

Returns a list of reasons for partial failures within an AutoML job.

(structure)

The reason for a partial failure of an AutoML job.

PartialFailureMessage -> (string)

The message containing the reason for a partial failure of an AutoML job.

BestCandidate -> (structure)

The best model candidate selected by SageMaker Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

CandidateName -> (string)

The name of the candidate.

FinalAutoMLJobObjectiveMetric -> (structure)

The best candidate result from an AutoML training job.

Type -> (string)

The type of metric with the best result.

MetricName -> (string)

The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .

Value -> (float)

The value of the metric with the best result.

ObjectiveStatus -> (string)

The objective’s status.

CandidateSteps -> (list)

Information about the candidate’s steps.

(structure)

Information about the steps for a candidate and what step it is working on.

CandidateStepType -> (string)

Whether the candidate is at the transform, training, or processing step.

CandidateStepArn -> (string)

The ARN for the candidate’s step.

CandidateStepName -> (string)

The name for the candidate’s step.

CandidateStatus -> (string)

The candidate’s status.

InferenceContainers -> (list)

Information about the inference container definitions.

(structure)

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see .

Image -> (string)

The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .

ModelDataUrl -> (string)

The location of the model artifacts. For more information, see .

Environment -> (map)

The environment variables to set in the container. For more information, see .

key -> (string)

value -> (string)

CreationTime -> (timestamp)

The creation time.

EndTime -> (timestamp)

The end time.

LastModifiedTime -> (timestamp)

The last modified time.

FailureReason -> (string)

The failure reason.

CandidateProperties -> (structure)

The properties of an AutoML candidate job.

CandidateArtifactLocations -> (structure)

The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

Explainability -> (string)

The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

ModelInsights -> (string)

The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

CandidateMetrics -> (list)

Information about the candidate metrics for an AutoML job.

(structure)

Information about the metric for a candidate produced by an AutoML job.

MetricName -> (string)

The name of the metric.

Value -> (float)

The value of the metric.

Set -> (string)

The dataset split from which the AutoML job produced the metric.

StandardMetricName -> (string)

The name of the standard metric.

Note

For definitions of the standard metrics, see ` Autopilot candidate metrics https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics`__ .

AutoMLJobStatus -> (string)

Returns the status of the AutoML job.

AutoMLJobSecondaryStatus -> (string)

Returns the secondary status of the AutoML job.

GenerateCandidateDefinitionsOnly -> (boolean)

Indicates whether the output for an AutoML job generates candidate definitions only.

AutoMLJobArtifacts -> (structure)

Returns information on the job’s artifacts found in AutoMLJobArtifacts .

CandidateDefinitionNotebookLocation -> (string)

The URL of the notebook location.

DataExplorationNotebookLocation -> (string)

The URL of the notebook location.

ResolvedAttributes -> (structure)

This contains ProblemType , AutoMLJobObjective , and CompletionCriteria . If you do not provide these values, they are auto-inferred. If you do provide them, the values used are the ones you provide.

AutoMLJobObjective -> (structure)

Specifies a metric to minimize or maximize as the objective of a job.

MetricName -> (string)

The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.

Here are the options:

Accuracy

The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for both binary and multiclass classification. Accuracy measures how close the predicted class values are to the actual values. Values for accuracy metrics vary between zero (0) and one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect inaccuracy.

AUC

The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms that return probabilities, such as logistic regression. To map the probabilities into classifications, these are compared against a threshold value.

The relevant curve is the receiver operating characteristic curve (ROC curve). The ROC curve plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives, but more false negatives.

AUC is the area under this ROC curve. Therefore, AUC provides an aggregated measure of the model performance across all possible classification thresholds. AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a score of one half (0.5) indicates that the prediction is not better than a random classifier.

BalancedAccuracy

BalancedAccuracy is a metric that measures the ratio of accurate predictions to all predictions. This ratio is calculated after normalizing true positives (TP) and true negatives (TN) by the total number of positive (P) and negative (N) values. It is used in both binary and multiclass classification and is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1. BalancedAccuracy gives a better measure of accuracy when the number of positives or negatives differ greatly from each other in an imbalanced dataset. For example, when only 1% of email is spam.

F1

The F1 score is the harmonic mean of the precision and recall, defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class, and false when they do not.

Precision is the ratio of the true positive predictions to all positive predictions, and it includes the false positives in a dataset. Precision measures the quality of the prediction when it predicts the positive class.

Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances. Recall measures how completely a model predicts the actual class members in a dataset.

F1 scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.

F1macro

The F1macro score applies F1 scoring to multiclass classification problems. It does this by calculating the precision and recall, and then taking their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro averages the individual scores to obtain the F1macro score. F1macro scores vary between 0 and 1. A score of 1 indicates the best possible performance, and 0 indicates the worst.

MAE

The mean absolute error (MAE) is a measure of how different the predicted and actual values are, when they’re averaged over all values. MAE is commonly used in regression analysis to understand model prediction error. If there is linear regression, MAE represents the average distance from a predicted line to the actual value. MAE is defined as the sum of absolute errors divided by the number of observations. Values range from 0 to infinity, with smaller numbers indicating a better model fit to the data.

MSE

The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive. The better a model is at predicting the actual values, the smaller the MSE value is

Precision

Precision measures how well an algorithm predicts the true positives (TP) out of all of the positives that it identifies. It is defined as follows: Precision = TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary classification. Precision is an important metric when the cost of a false positive is high. For example, the cost of a false positive is very high if an airplane safety system is falsely deemed safe to fly. A false positive (FP) reflects a positive prediction that is actually negative in the data.

PrecisionMacro

The precision macro computes precision for multiclass classification problems. It does this by calculating precision for each class and averaging scores to obtain precision for several classes. PrecisionMacro scores range from zero (0) to one (1). Higher scores reflect the model’s ability to predict true positives (TP) out of all of the positives that it identifies, averaged across multiple classes.

R2

R2, also known as the coefficient of determination, is used in regression to quantify how much a model can explain the variance of a dependent variable. Values range from one (1) to negative one (-1). Higher numbers indicate a higher fraction of explained variability. R2 values close to zero (0) indicate that very little of the dependent variable can be explained by the model. Negative values indicate a poor fit and that the model is outperformed by a constant function. For linear regression, this is a horizontal line.

Recall

Recall measures how well an algorithm correctly predicts all of the true positives (TP) in a dataset. A true positive is a positive prediction that is also an actual positive value in the data. Recall is defined as follows: Recall = TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better ability of the model to predict true positives (TP) in the data, and is used in binary classification.

Recall is important when testing for cancer because it’s used to find all of the true positives. A false positive (FP) reflects a positive prediction that is actually negative in the data. It is often insufficient to measure only recall, because predicting every output as a true positive will yield a perfect recall score.

RecallMacro

The RecallMacro computes recall for multiclass classification problems by calculating recall for each class and averaging scores to obtain recall for several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the model’s ability to predict true positives (TP) in a dataset. Whereas, a true positive reflects a positive prediction that is also an actual positive value in the data. It is often insufficient to measure only recall, because predicting every output as a true positive will yield a perfect recall score.

RMSE

Root mean squared error (RMSE) measures the square root of the squared difference between predicted and actual values, and it’s averaged over all values. It is used in regression analysis to understand model prediction error. It’s an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.

If you do not specify a metric explicitly, the default behavior is to automatically use:

• MSE : for regression.

• F1 : for binary classification

• Accuracy : for multiclass classification.

ProblemType -> (string)

The problem type.

CompletionCriteria -> (structure)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum runtime, in seconds, an AutoML job has to complete.

If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, will not be completed.

ModelDeployConfig -> (structure)

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

AutoGenerateEndpointName -> (boolean)

Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

Note

If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

EndpointName -> (string)

Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

Note

Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

ModelDeployResult -> (structure)

Provides information about endpoint for the model deployment.

EndpointName -> (string)

The name of the endpoint to which the model has been deployed.

Note

If model deployment fails, this field is omitted from the response.