Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
H2O.ai H2O-3 GBM Algorithm
By:
Latest Version:
0.1
Gradient Boosting Machine from H2O-3 Core Library
Product Overview
Gradient Boosting Machine (for Regression and Classification) is a forward learning ensemble method. The guiding heuristic is that good predictive results can be obtained through increasingly refined approximations. H2O’s GBM sequentially builds regression trees on all the features of the dataset in a fully distributed way - each tree is built in parallel.
Key Data
Version
By
Type
Algorithm
Highlights
H2O’s Gradient Boosting Algorithms follow the algorithm specified by Hastie et al (2001)
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$0.00/hr
running on ml.c5.2xlarge
Model Realtime Inference$0.00/hr
running on ml.c5.2xlarge
Model Batch Transform$0.00/hr
running on ml.c5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$0.408/host/hr
running on ml.c5.2xlarge
SageMaker Realtime Inference$0.408/host/hr
running on ml.c5.2xlarge
SageMaker Batch Transform$0.408/host/hr
running on ml.c5.2xlarge
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.c5.2xlarge Vendor Recommended | $0.00 | |
ml.c5.4xlarge | $0.00 | |
ml.c5.9xlarge | $0.00 | |
ml.c5.18xlarge | $0.00 | |
ml.c4.2xlarge | $0.00 | |
ml.c4.4xlarge | $0.00 | |
ml.c4.8xlarge | $0.00 | |
ml.m5.xlarge | $0.00 | |
ml.m5.2xlarge | $0.00 | |
ml.m5.4xlarge | $0.00 | |
ml.m5.12xlarge | $0.00 | |
ml.m5.24xlarge | $0.00 | |
ml.m4.2xlarge | $0.00 | |
ml.m4.4xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
See documentation for list of all available parameters that can be passed to the algorithm. NOTES: only parameter required is "training" hyperparameter. Please make sure to define "distribution" if the expected target is categorical. Or be sure to define "categorical_columns" with the specific categorical columns in the dataset.
Metrics
Name | Regex |
|---|---|
AUC | AUC: ([0-9.]*) |
MSE | MSE: ([0-9.]*) |
RMSE | RMSE: ([0-9.]*) |
auc_pr | auc_pr: ([0-9.]*) |
LogLoss | LogLoss: ([0-9.]*) |
Gini | Gini: ([0-9.]*) |
Channel specification
Fields marked with * are required
training
*training data
Input modes: File
Content types: csv
Compression types: None
Hyperparameters
Fields marked with * are required
training
*Training Parameters: distribution?, categorical_columns?, target?
Type: FreeText
Tunable: No
balance_classes
Balance training data class counts via over/under-sampling
Type: Categorical
Tunable: No
categorical_encoding
One of: auto, enum, one_hot_internal, one_hot_explicit, binary, eigen, label_encoder, sort_by_response, enum_limited (default: auto).
Type: FreeText
Tunable: No
class_sampling_factors
Desired over/under-sampling ratios per class (in lexicographic order). (ex. '1.0,1.5,1.7')
Type: FreeText
Tunable: No
col_sample_rate
Column sample rate (from 0.0 to 1.0)
Type: Continuous
Tunable: No
col_sample_rate_change_per_level
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
Type: Continuous
Tunable: No
col_sample_rate_per_tree
Column sample rate per tree (from 0.0 to 1.0)
Type: Continuous
Tunable: No
distribution
Distribution function
Type: FreeText
Tunable: No
fold_assignment
Cross-validation fold assignment scheme, if fold_column is not specified.
Type: FreeText
Tunable: No
fold_column
Column with cross-validation fold index assignment per observation.
Type: FreeText
Tunable: No
histogram_type
What type of histogram to use for finding optimal split points
Type: FreeText
Tunable: No
huber_alpha
Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
Type: Continuous
Tunable: No
ignore_const_cols
Ignore constant columns.
Type: Categorical
Tunable: No
ignored_columns
Names of columns to ignore for training
Type: FreeText
Tunable: No
learn_rate
Learning rate (from 0.0 to 1.0)
Type: Continuous
Tunable: No
learn_rate_annealing
Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999)
Type: Continuous
Tunable: No
max_abs_leafnode_pred
Maximum absolute value of a leaf node prediction
Type: Continuous
Tunable: No
max_after_balance_size
Maximum relative size of the training data after balancing class counts
Type: Continuous
Tunable: No
max_depth
Maximum tree depth.
Type: Integer
Tunable: No
max_hit_ratio_k
Maximum number (top K) of predictions to use for hit ratio computation
Type: Integer
Tunable: No
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Type: Continuous
Tunable: No
min_rows
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type: Integer
Tunable: No
min_split_improvement
Minimum relative improvement in squared error reduction for a split to happen
Type: Integer
Tunable: No
nbins
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
Type: Integer
Tunable: No
nbins_cats
For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.
Type: Integer
Tunable: No
nbins_top_level
For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
Type: Integer
Tunable: No
nfolds
Number of folds for K-fold cross-validation (0 to disable or >= 2).
Type: Integer
Tunable: No
ntrees
Number of trees.
Type: Integer
Tunable: No
offset_column
Offset column. This will be added to the combination of columns before applying the link function.
Type: FreeText
Tunable: No
pred_noise_bandwidth
Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions
Type: Continuous
Tunable: No
quantile_alpha
Desired quantile for Quantile regression, must be between 0 and 1.
Type: Continuous
Tunable: No
sample_rate
Row sample rate per tree (from 0.0 to 1.0)
Type: Continuous
Tunable: No
sample_rate_per_class
A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree, (ex. '1.3,1.1,0.5')
Type: FreeText
Tunable: No
score_each_iteration
Whether to score during each iteration of model training.
Type: Categorical
Tunable: No
score_tree_interval
Score the model after every so many trees. Disabled if set to 0.
Type: Integer
Tunable: No
seed
Seed for pseudo random number generator
Type: Integer
Tunable: No
stopping_metric
One of: auto, deviance, logloss, mse, rmse, mae, rmsle, auc, lift_top_group, misclassification, mean_per_class_error (default: auto).
Type: FreeText
Tunable: No
stopping_rounds
Early stopping based on convergence of stopping_metric.
Type: Integer
Tunable: No
stopping_tolerance
Relative tolerance for metric-based stopping criterion
Type: Continuous
Tunable: No
tweedie_power
tweedie power
Type: Continuous
Tunable: No
weights_column
Column with observation weights.
Type: FreeText
Tunable: No
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Support Information
AWS Infrastructure
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Learn MoreRefund Policy
There is no refund policy as the algorithm is offered for free
Customer Reviews
Anonymous
View allOnly loads JSON
Aug 9, 2019Verified purchase review from AWS Marketplace
This model does not train because it tries to load JSON data only. I inputted a .csv but it did not work.
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