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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 Automatic Model Tuning.
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: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

Minimize 
test:binary_classification_ accuracy 
The accuracy of the final model on the test dataset. 
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. 
Maximize 
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

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

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 
Minimize 
validation:binary_classific ation_accuracy 
The accuracy of the final model on the validation dataset. 
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 
Maximize 
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

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

Maximize 
Tuning Linear Learner Hyperparameters
You can tune a linear learner model with the following hyperparameters.
Parameter Name  Parameter Type  Recommended Ranges 

wd 


l1 


learning_rate 


mini_batch_size 


use_bias 


positive_example_weight_mult 

