Tune an XGBoost 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 training and validation datasets. You choose three types of hyperparameters:

a learning
objective
function to optimize during model training 
an
eval_metric
to use to evaluate model performance during validation 
a set of hyperparameters and a range of values for each to use when tuning the model automatically
You choose the evaluation metric from set of evaluation 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 evaluation metric.
Automatic model tuning for XGBoost 0.90 is only available from the Amazon SageMaker SDKs, not from the SageMaker console.
For more information about model tuning, see Perform Automatic Model Tuning.
Evaluation Metrics Computed by the XGBoost Algorithm
The XGBoost algorithm computes the following metrics to use for model validation.
When tuning the model, choose one of these metrics to evaluate the model. For full
list of valid eval_metric
values, refer to XGBoost Learning Task Parameters
Metric Name  Description  Optimization Direction 

validation:accuracy 
Classification rate, calculated as #(right)/#(all cases). 
Maximize 
validation:auc 
Area under the curve. 
Maximize 
validation:error 
Binary classification error rate, calculated as #(wrong cases)/#(all cases). 
Minimize 
validation:f1 
Indicator of classification accuracy, calculated as the harmonic mean of precision and recall. 
Maximize 
validation:logloss 
Negative loglikelihood. 
Minimize 
validation:mae 
Mean absolute error. 
Minimize 
validation:map 
Mean average precision. 
Maximize 
validation:merror 
Multiclass classification error rate, calculated as #(wrong cases)/#(all cases). 
Minimize 
validation:mlogloss 
Negative loglikelihood for multiclass classification. 
Minimize 
validation:mse 
Mean squared error. 
Minimize 
validation:ndcg 
Normalized Discounted Cumulative Gain. 
Maximize 
validation:rmse 
Root mean square error. 
Minimize 
Tunable XGBoost Hyperparameters
Tune the XGBoost model with the following hyperparameters. The hyperparameters
that have the greatest effect on optimizing the XGBoost evaluation metrics are:
alpha
, min_child_weight
, subsample
,
eta
, and num_round
.
Parameter Name  Parameter Type  Recommended Ranges 

alpha 
ContinuousParameterRanges 
MinValue: 0, MaxValue: 1000 
colsample_bylevel 
ContinuousParameterRanges 
MinValue: 0.1, MaxValue: 1 
colsample_bynode 
ContinuousParameterRanges 
MinValue: 0.1, MaxValue: 1 
colsample_bytree 
ContinuousParameterRanges 
MinValue: 0.5, MaxValue: 1 
eta 
ContinuousParameterRanges 
MinValue: 0.1, MaxValue: 0.5 
gamma 
ContinuousParameterRanges 
MinValue: 0, MaxValue: 5 
lambda 
ContinuousParameterRanges 
MinValue: 0, MaxValue: 1000 
max_delta_step 
IntegerParameterRanges 
[0, 10] 
max_depth 
IntegerParameterRanges 
[0, 10] 
min_child_weight 
ContinuousParameterRanges 
MinValue: 0, MaxValue: 120 
num_round 
IntegerParameterRanges 
[1, 4000] 
subsample 
ContinuousParameterRanges 
MinValue: 0.5, MaxValue: 1 