Tune a linear learner model

Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the objective metric.

The linear learner algorithm also has an internal mechanism for tuning hyperparameters separate from the automatic model tuning feature described here. By default, the linear learner algorithm tunes hyperparameters by training multiple models in parallel. When you use automatic model tuning, the linear learner internal tuning mechanism is turned off automatically. This sets the number of parallel models, num_models, to 1. The algorithm ignores any value that you set for num_models.

For more information about model tuning, see Perform Automatic Model Tuning with SageMaker.

Metrics computed by the linear learner algorithm

The linear learner algorithm reports the metrics in the following table, which are computed during training. Choose one of them as the objective metric. To avoid overfitting, we recommend tuning the model against a validation metric instead of a training metric.

Metric Name	Description	Optimization Direction
test:absolute_loss	The absolute loss of the final model on the test dataset. This objective metric is only valid for regression.	Minimize
test:binary_classification_accuracy	The accuracy of the final model on the test dataset. This objective metric is only valid for binary classification.	Maximize
test:binary_f_beta	The F-beta score of the final model on the test dataset. By default, it is the F1 score, which is the harmonic mean of precision and recall. This objective metric is only valid for binary classification.	Maximize
test:dcg	The discounted cumulative gain of the final model on the test dataset. This objective metric is only valid for multiclass classification.	Maximize
test:macro_f_beta	The F-beta score of the final model on the test dataset. This objective metric is only valid for multiclass classification.	Maximize
test:macro_precision	The precision score of the final model on the test dataset. This objective metric is only valid for multiclass classification.	Maximize
test:macro_recall	The recall score of the final model on the test dataset. This objective metric is only valid for multiclass classification.	Maximize
test:mse	The mean square error of the final model on the test dataset. This objective metric is only valid for regression.	Minimize
test:multiclass_accuracy	The accuracy of the final model on the test dataset. This objective metric is only valid for multiclass classification.	Maximize
test:multiclass_top_k_accuracy	The accuracy among the top k labels predicted on the test dataset. If you choose this metric as the objective, we recommend setting the value of k using the accuracy_top_k hyperparameter. This objective metric is only valid for multiclass classification.	Maximize
test:objective_loss	The mean value of the objective loss function on the test dataset after the model is trained. By default, the loss is logistic loss for binary classification and squared loss for regression. To set the loss to other types, use the loss hyperparameter.	Minimize
test:precision	The precision of the final model on the test dataset. If you choose this metric as the objective, we recommend setting a target recall by setting the binary_classifier_model_selection hyperparameter to precision_at_target_recall and setting the value for the target_recall hyperparameter. This objective metric is only valid for binary classification.	Maximize
test:recall	The recall of the final model on the test dataset. If you choose this metric as the objective, we recommend setting a target precision by setting the binary_classifier_model_selection hyperparameter to recall_at_target_precision and setting the value for the target_precision hyperparameter. This objective metric is only valid for binary classification.	Maximize
test:roc_auc_score	The area under receiving operating characteristic curve (ROC curve) of the final model on the test dataset. This objective metric is only valid for binary classification.	Maximize
validation:absolute_loss	The absolute loss of the final model on the validation dataset. This objective metric is only valid for regression.	Minimize
validation:binary_classification_accuracy	The accuracy of the final model on the validation dataset. This objective metric is only valid for binary classification.	Maximize
validation:binary_f_beta	The F-beta score of the final model on the validation dataset. By default, the F-beta score is the F1 score, which is the harmonic mean of the validation:precision and validation:recall metrics. This objective metric is only valid for binary classification.	Maximize
validation:dcg	The discounted cumulative gain of the final model on the validation dataset. This objective metric is only valid for multiclass classification.	Maximize
validation:macro_f_beta	The F-beta score of the final model on the validation dataset. This objective metric is only valid for multiclass classification.	Maximize
validation:macro_precision	The precision score of the final model on the validation dataset. This objective metric is only valid for multiclass classification.	Maximize
validation:macro_recall	The recall score of the final model on the validation dataset. This objective metric is only valid for multiclass classification.	Maximize
validation:mse	The mean square error of the final model on the validation dataset. This objective metric is only valid for regression.	Minimize
validation:multiclass_accuracy	The accuracy of the final model on the validation dataset. This objective metric is only valid for multiclass classification.	Maximize
validation:multiclass_top_k_accuracy	The accuracy among the top k labels predicted on the validation dataset. If you choose this metric as the objective, we recommend setting the value of k using the accuracy_top_k hyperparameter. This objective metric is only valid for multiclass classification.	Maximize
validation:objective_loss	The mean value of the objective loss function on the validation dataset every epoch. By default, the loss is logistic loss for binary classification and squared loss for regression. To set loss to other types, use the loss hyperparameter.	Minimize
validation:precision	The precision of the final model on the validation dataset. If you choose this metric as the objective, we recommend setting a target recall by setting the binary_classifier_model_selection hyperparameter to precision_at_target_recall and setting the value for the target_recall hyperparameter. This objective metric is only valid for binary classification.	Maximize
validation:recall	The recall of the final model on the validation dataset. If you choose this metric as the objective, we recommend setting a target precision by setting the binary_classifier_model_selection hyperparameter to recall_at_target_precision and setting the value for the target_precision hyperparameter. This objective metric is only valid for binary classification.	Maximize
validation:rmse	The root mean square error of the final model on the validation dataset. This objective metric is only valid for regression.	Minimize
validation:roc_auc_score	The area under receiving operating characteristic curve (ROC curve) of the final model on the validation dataset. This objective metric is only valid for binary classification.	Maximize

Tuning linear learner hyperparameters

You can tune a linear learner model with the following hyperparameters.

Parameter Name	Parameter Type	Recommended Ranges
wd	ContinuousParameterRanges	MinValue: 1e-7, MaxValue: 1
l1	ContinuousParameterRanges	MinValue: 1e-7, MaxValue: 1
learning_rate	ContinuousParameterRanges	MinValue: 1e-5, MaxValue: 1
mini_batch_size	IntegerParameterRanges	MinValue: 100, MaxValue: 5000
use_bias	CategoricalParameterRanges	[True, False]
positive_example_weight_mult	ContinuousParameterRanges	MinValue: 1e-5, MaxValue: 1e5