Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
Required? | True |
Position? | 1 |
Accept pipeline input? | True (ByValue, ByPropertyName) |
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:
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. When the data contains outliers, they tend to dominate the MSE, which might cause subpar prediction performance.Accuracy
: The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect accuracy and zero indicates perfect inaccuracy.F1
: The F1 score is the harmonic mean of the precision and 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 (including the false positives) in a data set and 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 and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates the best possible performance and zero the worst.AUC
: The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that 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 receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random classifier. Values under one half predict less accurately than a random predictor. But such consistently bad predictors can simply be inverted to obtain better than random predictors.F1macro
: The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then, use these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE
: for regression.F1
: for binary classificationAccuracy
: for multiclass classification.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-CandidateGenerationConfig_FeatureSpecificationS3Uri <
String>
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job (optional). This file should be in json format as shown below: { "FeatureAttributeNames":["col1", "col2", ...] }
.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.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_CandidateGenerationConfig_FeatureSpecificationS3Uri |
-CompletionCriteria_MaxAutoMLJobRuntimeInSecond <
Int32>
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.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_CompletionCriteria_MaxAutoMLJobRuntimeInSeconds |
-CompletionCriteria_MaxCandidate <
Int32>
The maximum number of times a training job is allowed to run.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_CompletionCriteria_MaxCandidates |
-CompletionCriteria_MaxRuntimePerTrainingJobInSecond <
Int32>
The maximum time, in seconds, that each training job is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_CompletionCriteria_MaxRuntimePerTrainingJobInSeconds |
-DataSplitConfig_ValidationFraction <
Single>
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.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_DataSplitConfig_ValidationFraction |
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-GenerateCandidateDefinitionsOnly <
Boolean>
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig
supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-ModelDeployConfig_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
.If you set AutoGenerateEndpointName
to True
, do not specify the EndpointName
; otherwise a 400 error is thrown.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-ModelDeployConfig_EndpointName <
String>
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.Specify the EndpointName
if and only if you set AutoGenerateEndpointName
to False
; otherwise a 400 error is thrown.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-OutputDataConfig_KmsKeyId <
String>
The Amazon Web Services KMS encryption key ID.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-OutputDataConfig_S3OutputPath <
String>
The Amazon S3 output path. Must be 128 characters or less.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Changes the cmdlet behavior to return the value passed to the AutoMLJobName parameter. The -PassThru parameter is deprecated, use -Select '^AutoMLJobName' instead. This parameter will be removed in a future version.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
The ARN of the role that is used to access the data.
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
-SecurityConfig_EnableInterContainerTrafficEncryption <
Boolean>
Whether to use traffic encryption between the container layers.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_SecurityConfig_EnableInterContainerTrafficEncryption |
-SecurityConfig_VolumeKmsKeyId <
String>
The key used to encrypt stored data.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_SecurityConfig_VolumeKmsKeyId |
Use the -Select parameter to control the cmdlet output. The default value is 'AutoMLJobArn'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.SageMaker.Model.CreateAutoMLJobResponse). Specifying the name of a property of type Amazon.SageMaker.Model.CreateAutoMLJobResponse will result in that property being returned. Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | Tags |
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets
field.
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_SecurityConfig_VpcConfig_SecurityGroupIds |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AutoMLJobConfig_SecurityConfig_VpcConfig_Subnets |