Amazon SageMaker
Developer Guide

The AWS Documentation website is getting a new look!
Try it now and let us know what you think. Switch to the new look >>

You can return to the original look by selecting English in the language selector above.

XGBoost Release 0.72

This previous release of the Amazon SageMaker XGBoost algorithm is based on the 0.72 release. XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining the estimates of a set of simpler, weaker models. XGBoost has done remarkably well in machine learning competitions because it robustly handles a variety of data types, relationships, and distributions, and because of the large number of hyperparameters that can be tweaked and tuned for improved fits. This flexibility makes XGBoost a solid choice for problems in regression, classification (binary and multiclass), and ranking.

Customers should consider using the new release of XGBoost Algorithm. They can use it as an Amazon SageMaker built-in algorithm or as a framework to run scripts in their local environments as they would typically, for example, do with a Tensorflow deep learning framework. The new implementation has a smaller memory footprint, better logging, improved hyperparameter validation, and an expanded set of metrics. The earlier implementation of XGBoost remains available to customers if they need to postpone migrating to the new version. But this previous implementation will remain tied to the 0.72 release of XGBoost.

Input/Output Interface for the XGBoost Release 0.72

Gradient boosting operates on tabular data, with the rows representing observations, one column representing the target variable or label, and the remaining columns representing features.

The Amazon SageMaker implementation of XGBoost supports CSV and libsvm formats for training and inference:

  • For Training ContentType, valid inputs are text/libsvm (default) or text/csv.

  • For Inference ContentType, valid inputs are text/libsvm or (the default) text/csv.

Note

For CSV training, the algorithm assumes that the target variable is in the first column and that the CSV does not have a header record. For CSV inference, the algorithm assumes that CSV input does not have the label column.

For libsvm training, the algorithm assumes that the label is in the first column. Subsequent columns contain the zero-based index value pairs for features. So each row has the format: <label> <index0>:<value0> <index1>:<value1> ... Inference requests for libsvm may or may not have labels in the libsvm format.

This differs from other Amazon SageMaker algorithms, which use the protobuf training input format to maintain greater consistency with standard XGBoost data formats.

For CSV training input mode, the total memory available to the algorithm (Instance Count * the memory available in the InstanceType) must be able to hold the training dataset. For libsvm training input mode, it's not required, but we recommend it.

SageMaker XGBoost uses the Python pickle module to serialize/deserialize the model, which can be used for saving/loading the model.

To use a model trained with SageMaker XGBoost in open source XGBoost

  • Use the following Python code:

    import pickle as pkl model = pkl.load(open(model_file_path, 'rb')) # prediction with test data pred = model.predict(dtest)

To differentiate the importance of labelled data points use Instance Weight Supports

  • Amazon SageMaker XGBoost allows customers to differentiate the importance of labelled data points by assigning each instance a weight value. For text/libsvm input, customers can assign weight values to data instances by attaching them after the labels. For example, label:weight idx_0:val_0 idx_1:val_1.... For text/csv input, customers need to turn on the csv_weights flag in the parameters and attach weight values in the column after labels. For example: label,weight,val_0,val_1,...).

EC2 Instance Recommendation for the XGBoost Release 0.72

Amazon SageMaker XGBoost currently only trains using CPUs. It is a memory-bound (as opposed to compute-bound) algorithm. So, a general-purpose compute instance (for example, M4) is a better choice than a compute-optimized instance (for example, C4). Further, we recommend that you have enough total memory in selected instances to hold the training data. Although it supports the use of disk space to handle data that does not fit into main memory (the out-of-core feature available with the libsvm input mode), writing cache files onto disk slows the algorithm processing time.

XGBoost Release 0.72 Sample Notebooks

For a sample notebook that shows how to use the latest version of Amazon SageMaker XGBoost as a built-in algorithm to train and host a regression model, see Regression with Amazon SageMaker XGBoost algorithm. To use the 0.72 version of XGBoost, you need to change the version in the sample code to 0.72. For instructions how to create and access Jupyter notebook instances that you can use to run the example in Amazon SageMaker, see Use Notebook Instances. Once you have created a notebook instance and opened it, select the SageMaker Examples tab to see a list of all the Amazon SageMaker samples. The topic modeling example notebooks using the NTM algorithms are located in the Introduction to Amazon algorithms section. To open a notebook, click on its Use tab and select Create copy.

XGBoost Release 0.72 Hyperparameters

The following table contains the hyperparameters for the XGBoost algorithm. These are parameters that are set by users to facilitate the estimation of model parameters from data. The required hyperparameters that must be set are listed first, in alphabetical order. The optional hyperparameters that can be set are listed next, also in alphabetical order. The Amazon SageMaker XGBoost algorithm is an implementation of the open-source XGBoost package. Currently Amazon SageMaker supports version 0.72. For more detail about hyperparameter configuration for this version of XGBoost, see XGBoost Parameters.

Parameter Name Description
num_class

The number of classes.

Required if objective is set to multi:softmax or multi:softprob.

Valid values: integer

num_round

The number of rounds to run the training.

Required

Valid values: integer

alpha

L1 regularization term on weights. Increasing this value makes models more conservative.

Optional

Valid values: float

Default value: 0

base_score

The initial prediction score of all instances, global bias.

Optional

Valid values: float

Default value: 0.5

booster

Which booster to use. The gbtree and dart values use a tree-based model, while gblinear uses a linear function.

Optional

Valid values: String. One of gbtree, gblinear, or dart.

Default value: gbtree

colsample_bylevel

Subsample ratio of columns for each split, in each level.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

colsample_bytree

Subsample ratio of columns when constructing each tree.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

csv_weights

When this flag is enabled, XGBoost differentiates the importance of instances for csv input by taking the second column (the column after labels) in training data as the instance weights.

Optional

Valid values: 0 or 1

Default value: 0

early_stopping_rounds

The model trains until the validation score stops improving. Validation error needs to decrease at least every early_stopping_rounds to continue training. Amazon SageMaker hosting uses the best model for inference.

Optional

Valid values: integer

Default value: -

eta

Step size shrinkage used in updates to prevent overfitting. After each boosting step, you can directly get the weights of new features. The eta parameter actually shrinks the feature weights to make the boosting process more conservative.

Optional

Valid values: Float. Range: [0,1].

Default value: 0.3

eval_metric

Evaluation metrics for validation data. A default metric is assigned according to the objective:

  • rmse: for regression

  • error: for classification

  • map: for ranking

For a list of valid inputs, see XGBoost Parameters.

Optional

Valid values: string

Default value: Default according to objective.

gamma

Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger, the more conservative the algorithm is.

Optional

Valid values: Float. Range: [0,∞).

Default value: 0

grow_policy

Controls the way that new nodes are added to the tree. Currently supported only if tree_method is set to hist.

Optional

Valid values: String. Either depthwise or lossguide.

Default value: depthwise

lambda

L2 regularization term on weights. Increasing this value makes models more conservative.

Optional

Valid values: float

Default value: 1

lambda_bias

L2 regularization term on bias.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0

max_bin

Maximum number of discrete bins to bucket continuous features. Used only if tree_method is set to hist.

Optional

Valid values: integer

Default value: 256

max_delta_step

Maximum delta step allowed for each tree's weight estimation. When a positive integer is used, it helps make the update more conservative. The preferred option is to use it in logistic regression. Set it to 1-10 to help control the update.

Optional

Valid values: Integer. Range: [0,∞).

Default value: 0

max_depth

Maximum depth of a tree. Increasing this value makes the model more complex and likely to be overfit. 0 indicates no limit. A limit is required when grow_policy=depth-wise.

Optional

Valid values: Integer. Range: [0,∞)

Default value: 6

max_leaves

Maximum number of nodes to be added. Relevant only if grow_policy is set to lossguide.

Optional

Valid values: integer

Default value: 0

min_child_weight

Minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, the building process gives up further partitioning. In linear regression models, this simply corresponds to a minimum number of instances needed in each node. The larger the algorithm, the more conservative it is.

Optional

Valid values: Float. Range: [0,∞).

Default value: 1

normalize_type

Type of normalization algorithm.

Optional

Valid values: Either tree or forest.

Default value: tree

nthread

Number of parallel threads used to run xgboost.

Optional

Valid values: integer

Default value: Maximum number of threads.

objective

Specifies the learning task and the corresponding learning objective. Examples: reg:linear, reg:logistic, multi:softmax. For a full list of valid inputs, refer to XGBoost Parameters.

Optional

Valid values: string

Default value: reg:linear

one_drop

When this flag is enabled, at least one tree is always dropped during the dropout.

Optional

Valid values: 0 or 1

Default value: 0

process_type

The type of boosting process to run.

Optional

Valid values: String. Either default or update.

Default value: default

rate_drop

The dropout rate that specifies the fraction of previous trees to drop during the dropout.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0.0

refresh_leaf

This is a parameter of the 'refresh' updater plug-in. When set to true (1), tree leaves and tree node stats are updated. When set to false(0), only tree node stats are updated.

Optional

Valid values: 0/1

Default value: 1

sample_type

Type of sampling algorithm.

Optional

Valid values: Either uniform or weighted.

Default value: uniform

scale_pos_weight

Controls the balance of positive and negative weights. It's useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases).

Optional

Valid values: float

Default value: 1

seed

Random number seed.

Optional

Valid values: integer

Default value: 0

silent

0 means print running messages, 1 means silent mode.

Valid values: 0 or 1

Optional

Default value: 0

sketch_eps

Used only for approximate greedy algorithm. This translates into O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.

Optional

Valid values: Float, Range: [0, 1].

Default value: 0.03

skip_drop

Probability of skipping the dropout procedure during a boosting iteration.

Optional

Valid values: Float. Range: [0.0, 1.0].

Default value: 0.0

subsample

Subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly collects half of the data instances to grow trees. This prevents overfitting.

Optional

Valid values: Float. Range: [0,1].

Default value: 1

tree_method

The tree construction algorithm used in XGBoost.

Optional

Valid values: One of auto, exact, approx, or hist.

Default value: auto

tweedie_variance_power

Parameter that controls the variance of the Tweedie distribution.

Optional

Valid values: Float. Range: (1, 2).

Default value: 1.5

updater

A comma-separated string that defines the sequence of tree updaters to run. This provides a modular way to construct and to modify the trees.

For a full list of valid inputs, please refer to XGBoost Parameters.

Optional

Valid values: comma-separated string.

Default value: grow_colmaker, prune

Tune an XGBoost Release 0.72 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.

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

Metrics Computed by the XGBoost Release 0.72 Algorithm

The XGBoost algorithm based on version 0.72 computes the following nine metrics during training. When tuning the model, choose one of these metrics as the objective to evaluate the model.

Metric Name Description Optimization Direction
validation:auc

Area under the curve.

Maximize

validation:error

Binary classification error rate, calculated as #(wrong cases)/#(all cases).

Minimize

validation:logloss

Negative log-likelihood.

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 log-likelihood for multiclass classification.

Minimize

validation:ndcg

Normalized Discounted Cumulative Gain.

Maximize

validation:rmse

Root mean square error.

Minimize

Tunable XGBoost Release 0.72 Hyperparameters

Tune the XGBoost model with the following hyperparameters. The hyperparameters that have the greatest effect on XGBoost objective 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_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