Migrating to H2O 3¶
H2O 3 offers a lot of improvements over H2O 2, including:
 Powerful Python APIs
 Flow, a brandnew intuitive web UI
 The ability to share, annotate, and modify workflows
 Versioned REST APIs with full metadata
 Spark integration using Sparkling Water
 Improved algorithm accuracy and speed
and much more! Overall, H2O has been retooled for better accuracy and performance and to provide additional functionality. If you’re a current user of H2O, we strongly encourage you to upgrade to the latest version to take advantage of the latest features and capabilities.
Please be aware that H2O 3 supersedes all previous versions of H2O as the primary version as of May 15th, 2015. Support for previous versions will be offered for a limited time, but there will no longer be any significant updates to the previous version of H2O.
The following information and links will inform you about what’s new and different and help you prepare to upgrade to H2O 3.
Overall, H2O 3 is more stable, elegant, and simplified, with additional capabilities not available in previous versions of H2O.
Algorithm Changes¶
Most of the algorithms available in previous versions of H2O have been improved in terms of speed and accuracy, and new algorithms have been and will continue to be added.
Note: The SpeeDRF model has been removed, as it was originally intended as an optimization for small data only. This optimization will be added to the Distributed Random Forest model automatically for small data in a future version of H2O.
Parsing Changes¶
In H2O Classic, the parser reads all the data and tries to guess the column type. In H2O 3, the parser reads a subset and makes a type guess for each column. In Flow, you can view the preliminary parse results in the Edit Column Names and Types area. To change the column type, select an option from the dropdown menu to the right of the column. H2O 3 can also automatically identify mixedtype columns; in H2O Classic, if one column is mixed integers or real numbers using a string, the output is blank.
Web UI Changes¶
Our web UI has been completely overhauled with a much more intuitive interface that is similar to IPython Notebook. Each pointandclick action is translated immediately into an individual workflow script that can be saved for later interactive and offline use. As a result, you can now revise and rerun your workflows easily, and can even add comments and rich media.
For more information, refer to our Getting Started with Flow guide, which comprehensively documents how to use Flow. You can also view this brief video, which provides an overview of Flow in action.
API Users¶
H2O’s new Python API allows Pythonistas to use H2O in their favorite environment. Using the Python command line or an integrated development environment like IPython Notebook, H2O users can control clusters and manage massive datasets quickly.
H2O’s REST API is the basis for the web UI (Flow), as well as the R and Python APIs, and is versioned for stability. It is also easier to understand and use, with full metadata available dynamically from the server, allowing for easier integration by developers.
Java Users¶
Generated Java REST classes ease REST API use by external programs running in a Java Virtual Machine (JVM).
As in previous versions of H2O, users can export trained models as Java objects for easy integration into JVM applications. H2O is currently the only ML tool that provides this capability, making it the data science tool of choice for enterprise developers.
R Users¶
If you use H2O primarily in R, be aware that as a result of the improvements to the R package for H2O scripts created using previous versions (Nunes 2.8.6.2 or prior) will require minor revisions to work with H2O 3.
To assist our R users in upgrading to H2O 3, a “shim” tool has been developed. The shim reviews your script, identifies deprecated or revised parameters and arguments, and suggests replacements.
Note: As of Slater v.3.2.0.10, this shim will no longer be available.
You can also review the Porting R Scripts section, which provides a sidebyside comparison of the algorithms in the previous version of H2O with H2O 3. It outlines the new, revised, and deprecated parameters for each algorithm, as well as the changes to the output.
Porting R Scripts¶
This document outlines how to port R scripts written in previous versions of H2O (Nunes 2.8.6.2 or prior, also known as “H2O Classic”) for compatibility with the new H2O 3 API. When upgrading from H2O to H2O 3, most functions are the same. However, there are some differences that will need to be resolved when porting any scripts that were originally created using H2O to H2O 3.
The original R script for H2O is listed first, followed by the updated script for H2O 3.
Some of the parameters have been renamed for consistency. For each algorithm, a table that describes the differences is provided.
For additional assistance within R, enter a question mark before the
command (for example, ?h2o.glm
).
There is also a “shim” available that will review R scripts created with previous versions of H2O, identify deprecated or renamed parameters, and suggest replacements. For more information, refer to the repo here.
Github Users¶
All users who pull directly from the H2O classic repo on Github should be aware that this repo will be renamed. To retain access to the original H2O (2.8.6.2 and prior) repository:
The simple way¶
This is the easiest way to change your local repo and is recommended for most users.
Enter
git remote v
to view a list of your repositories.Copy the address of your H2O classic repo (refer to the text in brackets below  your address will vary depending on your connection method):
H2O_UserMBP:h2o H2O_User$ git remote v origin https://{H2O_User@github.com}/h2oai/h2o.git (fetch) origin https://{H2O_User@github.com}/h2oai/h2o.git (push)
3. Enter git remote seturl origin {H2O_User@github.com}:h2oai/h2o2.git
,
where {H2O_User@github.com}
represents the address copied in the
previous step.
The more complicated way¶
This method involves editing the Github config file and should only be attempted by users who are confident enough with their knowledge of Github to do so.
Enter
vim .git/config
.Look for the
[remote "origin"]
section:[remote "origin"] url = https://H2O_User@github.com/h2oai/h2o.git fetch = +refs/heads/*:refs/remotes/origin/*
In the
url =
line, changeh2o.git
toh2o2.git
.Save the changes.
The latest version of H2O is stored in the h2o3
repository. All
previous links to this repo will still work, but if you would like to
manually update your Github configuration, follow the instructions
above, replacing h2o2
with h2o3
.
Changes from H2O 2.8 to H2O 3¶
h2o.exec
¶
The h2o.exec
command is no longer supported. Any workflows using
h2o.exec
must be revised to remove this command. If the H2O 3
workflow contains any parameters or commands from H2O Classic, errors
will result and the workflow will fail.
The purpose of h2o.exec
was to wrap expressions so that they could
be evaluated in a single \Exec2
call. For example,
h2o.exec(fr[,1] + 2/fr[,3])
and fr[,1] + 2/fr[,3]
produced the
same results in H2O. However, the first example makes a single REST call
and uses a single temp object, while the second makes several REST calls
and uses several temp objects.
Due to the improved architecture in H2O 3, the need to use
h2o.exec
has been eliminated, as the expression can be processed by
R as an “unwrapped” typical R expression.
Currently, the only known exception is when factor
is used in
conjunction with h2o.exec
. For example,
h2o.exec(fr$myIntCol < factor(fr$myIntCol))
would become
fr$myIntCol < as.factor(fr$myIntCol)
Note also that an array is not inside a string:
An int array is [1, 2, 3], not “[1, 2, 3]”.
A String array is [“f00”, “b4r”], not “[“f00”, “b4r”]”
Only string values are enclosed in double quotation marks ("
).
h2o.performance
¶
To access any exclusively binomial output, use h2o.performance
,
optionally with the corresponding accessor. The accessor can only use
the model metrics object created by h2o.performance
. Each accessor
is named for its corresponding field (for example, h2o.AUC
,
h2o.gini
, h2o.F1
). h2o.performance
supports all current
algorithms except for KMeans.
If you specify a data frame as a second parameter, H2O will use the specified data frame for scoring. If you do not specify a second parameter, the training metrics for the model metrics object are used.
xval
and validation
slots¶
The xval
slot has been removed, as nfolds
is not currently
supported.
The validation
slot has been merged with the model
slot.
Principal Components Regression (PCR)¶
Principal Components Regression (PCR) has also been deprecated. To obtain PCR values, create a Principal Components Analysis (PCA) model, then create a GLM model from the scored data from the PCA model.
Saving and Loading Models¶
Saving and loading a model from R is supported in version 3.0.0.18 and
later. H2O 3 uses the same binary serialization method as previous
versions of H2O, but saves the model and its dependencies into a
directory, with each object as a separate file. The save_CV
option
for available in previous versions of H2O has been deprecated, as
h2o.saveAll
and h2o.loadAll
are not currently supported. The
following commands are now supported:
h2o.saveModel
h2o.loadModel
Algorithm Updates
GBM¶
Nfold crossvalidation and grid search are supported in H2O 3.
Renamed GBM Parameters¶
The following parameters have been renamed, but retain the same functions:
H2O Classic Parameter Name  H2O 3 Parameter Name 

data 
training_frame 
key 
model_id 
n.trees 
ntrees 
interaction.depth 
max_depth 
n.minobsinnode 
min_rows 
shrinkage 
learn_rate 
n.bins 
nbins 
validation 
validation_frame 
balance.classes 
balance_classes 
max.after.balance.size 
max_after_balance_size 
Deprecated GBM Parameters¶
The following parameters have been removed:
group_split
: Bitset group splitting of categorical variables is now the default.importance
: Variable importances are now computed automatically and displayed in the model output.holdout.fraction
: The fraction of the training data to hold out for validation is no longer supported.grid.parallelism
: Specifying the number of parallel threads to run during a grid search is no longer supported.
New GBM Parameters¶
The following parameters have been added:
seed
: A random number to control sampling and initialization whenbalance_classes
is enabled.score_each_iteration
: Display error rate information after each tree in the requested set is built.build_tree_one_node
: Run on a single node to use fewer CPUs.
GBM Algorithm Comparison¶
H2O Classic  H2O 3 

h2o.gbm < function( 
h2o.gbm < function( 
x, 
x, 
y, 
y, 
data, 
training_frame, 
key = "", 
model_id, 
checkpoint 

distribution
= multinomial, 
distribution
= c("AUTO",
"gaussian",
bernoulli",
"multinomial",
"poisson", "gamma",
"tweedie"), 
tweedie_power = 1.5, 

n.trees = 10, 
ntrees = 50 
interaction.depth = 5, 
max_depth = 5 
n.minobsinnode = 10, 
min_rows = 10 
shrinkage = 0.1, 
learn_rate = 0.1, 
sample_rate = 1 

col_sample_rate = 1 

n.bins = 20, 
nbins = 20, 
nbins_top_level, 

nbins_cats = 1024, 

validation, 
validation_frame
= NULL, 
balance.classes
= FALSE, 
balance_classes
= FALSE, 
max.after.balance.size
= 5, 
max_after_balance_size
= 1, 
seed, 

build_tree_one_node
= FALSE, 

nfolds = 0, 

fold_column = NULL, 

fold_assignment =
c("AUTO", "Random",
"Modulo"), 

keep_cross_validation_predictions
= FALSE, 

score_each_iterations
= FALSE, 

stopping_rounds = 0, 

stopping_metric
= c("AUTO", "deviance",
"logloss", "MSE,
"AUC", "r2",
"misclassification"), 

stopping_tolerance
= 0.001, 

offset_column = NULL, 

weights_column = NULL, 

group_split = TRUE , 

importance = FALSE, 

holdout.fraction = 0, 

class.sampling.factors
= NULL, 

grid.parallelism = 1) 
Output¶
The following table provides the component name in H2O, the
corresponding component name in H2O 3 (if supported), and the model
type (binomial, multinomial, or all). Many components are now included
in h2o.performance
; for more information, refer to
h2o.performance.
H2O Classic  H2O 3  Model Type 

@model$priorDistribution 
all 

@model$params 
@allparameters 
all 
@model$err 
@model$scoring_history 
all 
@model$classification 
all 

@model$varimp 
@model$variable_importances 
all 
@model$confusion 
@model$training_metrics@metrics$cm$table 
binomial
and
multinomial 
@model$auc 
@model$training_metrics@metrics$AUC 
binomial 
@model$gini 
@model$training_metrics@metrics$Gini 
binomial 
@model$best_cutoff 
binomial 

@model$F1 
@model$training_metrics@metrics$thresholds_and_metric_scores$f1 
binomial 
@model$F2 
@model$training_metrics@metrics$thresholds_and_metric_scores$f2 
binomial 
@model$accuracy 
@model$training_metrics@metrics$thresholds_and_metric_scores$accuracy 
binomial 
@model$error 
binomial 

@model$precision 
@model$training_metrics@metrics$thresholds_and_metric_scores$precision 
binomial 
@model$recall 
@model$training_metrics@metrics$thresholds_and_metric_scores$recall 
binomial 
@model$mcc 
@model$training_metrics@metrics$thresholds_and_metric_scores$absolute_MCC 
binomial 
@model$max_per_class_err 
currently replaced by
@model$training_metrics@metrics$thresholds_and_metric_scores$min_per_class_correct 
binomial 
GLM¶
Renamed GLM Parameters¶
The following parameters have been renamed, but retain the same functions:
H2O Classic Parameter Name  H2O 3 Parameter Name 

data 
training_frame 
key 
model_id 
nlambda 
nlambdas 
lambda.min.ratio 
lambda_min_ratio 
iter.max 
max_iterations 
epsilon 
beta_epsilon 
Deprecated GLM Parameters¶
The following parameters have been removed:
return_all_lambda
: A logical value indicating whether to return every model built during the lambda search. (may be readded)higher_accuracy
: For improved accuracy, adjust thebeta_epsilon
value.strong_rules
: Discards predictors likely to have 0 coefficients prior to model building. (may be readded as enabled by default)non_negative
: Specify a nonnegative response. (may be readded)variable_importances
: Variable importances are now computed automatically and displayed in the model output. They have been renamed to Normalized Coefficient Magnitudes.disable_line_search
: This parameter has been deprecated, as it was mainly used for testing purposes.max_predictors
: Stops training the algorithm if the number of predictors exceeds the specified value. (may be readded)
New GLM Parameters¶
The following parameters have been added:
validation_frame
: Specify the validation dataset.solver
: Select IRLSM or LBFGS.
GLM Algorithm Comparison¶
H2O Classic  H2O 3 

h2o.glm < function( ) 
h2o.glm( 
x, 
x, 
y, 
y, 
data, 
training_frame, 
key = "", 
model_id, 
validation_frame = NULL 

iter.max = 100, 
max_iterations = 50, 
epsilon = 1e4 
beta_epsilon = 0 
strong_rules = TRUE, 

return_all_lambda = FALSE, 

intercept = TRUE, 
intercept = TRUE 
non_negative = FALSE, 

solver = c("IRLSM", "L_BFGS"), 

standardize = TRUE, 
standardize = TRUE, 
family, 
family = c("gaussian", "binomial",
multinomial", "poisson", "gamma", "tweedie") 
link, 
link = c("family_default", "identity",
"logit", "log", "inverse", "tweedie"), 
tweedie.p = ifelse(family ==
tweedie, 1.5, NA_real_) 
tweedie_variariance_power = NaN, 
tweedie_link_power = NaN 

alpha = 0.5, 
alpha = 0.5, 
prior = NULL 
prior = 0.0, 
lambda = 1e5, 
lambda = 1e5, 
lambda_search = FALSE, 
lambda_search = FALSE, 
nlambda = 1, 
nlambdas = 1, 
lambda.min.ratio = 1, 
lambda_min_ration = 1.0, 
use_all_factor_levels = FALSE 
use_all_factor_levels = FALSE 
nfolds = 0, 
nfolds = 0, 
fold_column = NULL, 

fold_assignment = c("AUTO", "Random",
Modulo"), 

keep_cross_validation_predictions = FALSE, 

beta_constraints = NULL, 
beta_constraints = NULL) 
higher_accuracy = FALSE, 

variable_importances = FALSE, 

disable_line_search = FALSE, 

offset = NULL, 
offset_column = NULL, 
weights_column = NULL, 

intercept = TRUE, 

max_predictors = 1) 
max_active_predictors = 1) 
Output¶
The following table provides the component name in H2O, the
corresponding component name in H2O 3 (if supported), and the model
type (binomial, multinomial, or all). Many components are now included
in h2o.performance
; for more information, refer to
h2o.performance.
H2O Classic  H2O 3  Model Type 

@model$params 
@allparameters 
all 
@model$coefficients 
@model$coefficients 
all 
@model$nomalized_coefficients 
@model$coefficients_table$norm_coefficients 
all 
@model$rank 
@model$rank 
all 
@model$iter 
@model$iter 
all 
@model$lambda 
all 

@model$deviance 
@model$residual_deviance 
all 
@model$null.deviance 
@model$null_deviance 
all 
@model$df.residual 
@model$residual_degrees_of_freedom 
all 
@model$df.null 
@model$null_degrees_of_freedom 
all 
@model$aic 
@model$AIC 
all 
@model$train.err 
binomial 

@model$prior 
binomial 

@model$thresholds 
@model$threshold 
binomial 
@model$best_threshold 
binomial 

@model$auc 
@model$AUC 
binomial 
@model$confusion 
binomial 
KMeans¶
Renamed KMeans Parameters¶
The following parameters have been renamed, but retain the same functions:
H2O Classic Parameter Name  H2O 3 Parameter Name 

data 
training_frame 
key 
model_id 
centers 
k 
cols 
x 
iter.max 
max_iterations 
normalize 
standardize 
Note In H2O, the normalize
parameter was disabled by default.
The standardize
parameter is enabled by default in H2O 3 to
provide more accurate results for datasets containing columns with large
values.
New KMeans Parameters¶
The following parameters have been added:
user
has been added as an additional option for theinit
parameter. Using this parameter forces the KMeans algorithm to start at the userspecified points.user_points
: Specify starting points for the KMeans algorithm.
KMeans Algorithm Comparison¶
H2O Classic  H2O 3 

h2o.kmeans < function( 
h2o.kmeans( 
data, 
training_frame, 
cols = '', 
x, 
centers, 
k, 
key = "", 
model_id, 
iter.max = 10, 
max_iterations = 1000, 
normalize = FALSE, 
standardize = TRUE, 
init = "none", seed=0, 
init = c("Furthest","Random", "PlusPlus"), seed, nfolds = 0, 
fold_column = NULL, 

fold_assignment = c("AUTO", "Random", "Modulo"), 

keep_cross_validation_predictions = FALSE) 
Output¶
The following table provides the component name in H2O and the corresponding component name in H2O 3 (if supported).
H2O Classic  H2O 3 

@model$params 
@allparameters 
@model$centers 
@model$centers 
@model$tot.withinss 
@model$tot_withinss 
@model$size 
@model$size 
@model$iter 
@model$iterations 
@model$_scoring_history 

@model$_model_summary 
Deep Learning¶
Note: If the results in the confusion matrix are incorrect, verify
that score_training_samples
is equal to 0. By default, only the
first 10,000 rows are included.
Renamed Deep Learning Parameters¶
The following parameters have been renamed, but retain the same functions:
H2O Classic Parameter Name  H2O 3 Parameter Name 

data 
training_frame 
key 
model_id 
validation 
validation_frame 
class.sampling.factors 
class_sampling_factors 
override_with_best_model 
overwrite_with_best_model 
dlmodel@model$valid_class_error 
@model$validation_metrics@$MSE 
Deprecated DL Parameters¶
The following parameters have been removed:
classification
: Classification is now inferred from the data type.holdout_fraction
: Fraction of the training data to hold out for validation.dlmodel@model$best_cutoff
: This output parameter has been removed.
New DL Parameters¶
The following parameters have been added:
export_weights_and_biases
: An additional option allowing users to export the raw weights and biases as H2O frames.
The following options for the loss
parameter have been added:
absolute
: Provides strong penalties for mispredictionshuber
: Can improve results for regression
DL Algorithm Comparison¶
H2O Classic  H2O 3 

h2o.deeplearning < function(x, 
h2o.deeplearning (x, 
y, 
y, 
data, 
training_frame, 
key = "", 
model_id = "", 
override_with_best_model, 
overwrite_with_best_model = true, 
classification = TRUE, 

nfolds = 0, 
nfolds = 0 
validation, 
validation_frame, 
holdout_fraction = 0, 

checkpoint = " " 
checkpoint, 
autoencoder, 
autoencoder = false, 
use_all_factor_levels, 
use_all_factor_levels = true 
activation, 
_activation = c("Rectifier", "Tanh",
"TanhWithDropout", "RectifierWithDropout",
"Maxout", "MaxoutWithDropout"), 
hidden, 
hidden= c(200, 200,) 
epochs, 
epochs = 10.0, 
train_samples_per_iteration, 
train_samples_per_iteration = 2, 
target_ratio_comm_to_comp = 0.05 

seed, 
_seed, 
adaptive_rate, 
adaptive_rate = true, 
rho, 
rho = 0.99, 
epsilon, 
epsilon = 1e08, 
rate, 
rate = .005, 
rate_annealing 
rate_annealing = 1e06, 
rate_decay, 
rate_decay = 1.0, 
momentum_start, 
momentum_start = 0, 
momentum_ramp, 
momentum_ramp = 1e+06, 
momentum_stable, 
momentum_stable = 0, 
nesterov_accelerated_gradient, 
nesterov_accelerated_gradient = true, 
input_dropout_ratio, 
input_dropout_ratio = 0.0, 
hidden_dropout_ratios, 
hidden_dropout_ratios, 
l1, 
l1 = 0.0, 
l2, 
l2 = 0.0, 
max_w2, 
max_w2 = Inf, 
initial_weight_distribution, 
initial_weight_distribution =
c("UniformAdaptive", "Uniform", "Normal"), 
initial_weight_scale, 
initial_weight_scale = 1.0, 
loss, 
loss = "Automatic", "Cross Entropy",
"Quadratic", "Absolute", "Huber"), 
distribution = c("AUTO", "gaussian", ``
``"bernoulli", "multinomial", "poisson",
"gamma", "tweedie", "laplace", "huber"), 

tweedie_power = 1.5, 

score_interval, 
score_interval = 5, 
score_training_samples, 
score_training_samples = 10000l, 
score_validation_samples, 
score_validation_samples = 0l, 
score_duty_cycle, 
score_duty_cycle = 0.1, 
classification_stop, 
classification_stop = 011 
regression_stop, 
regression_stop = 1e6, 
stopping_rounds = 5, 

stopping_metric = c("AUTO", "deviance",
"logloss", "MSE", "AUC", "r2",
"misclassification), 

stopping_tolerance = 0, 

quiet_mode, 
quiet_mode = falese, 
max_confusion_matrix_size, 
max_confusion_matrix_size, 
max_hit_ratio_k, 
max_hit_ratio_k, 
balance_clases, 
balance_classes = false, 
class_sampling_factors, 
class_sampling_factors, 
max_after_balance_size,, 
max_after_balance_size, 
score_validation_sampling, 
score_validation_sampling, 
diagnostics, 
diagnostics = true, 
variable_importances, 
variable_importances = false, 
fast_mode, 
fast_mode = true, 
ignore_const_cols, 
ignore_const_cols = true, 
force_load_balance, 
force_load_balance = true, 
replicate_training_data, 
replicate_training_data = true, 
single_node_mode, 
single_node_mode = false, 
shuffle_training_data, 
shuffle_training_data = false, 
sparse, 
sparse = false, 
col_major, 
col_major = false, 
max_categorical_features, 
max_categorical_features, 
reproducible) 
reproducible = FALSE, 
average_activation 
average_activatin = 0, 
sparsity_beta = 0 

export_weights_and_biases = FALSE, 

offset_column = NULL, 

weights_column = NULL, 

nfolds = 0, 

fold_column = NULL, 

fold_assignment = c("AUTO", "Random",
Modulo"), 

keep_cross_validation_predictions = FALSE) 
Output¶
The following table provides the component name in H2O, the
corresponding component name in H2O 3 (if supported), and the model
type (binomial, multinomial, or all). Many components are now included
in h2o.performance
; for more information, refer to
h2o.performance.
H2O Classic  H2O 3  Model Type 

@model$priorDistribution 
all 

@model$params 
@allparameters 
all 
@model$train_class_error 
@model$training_metrics@metrics@$MSE 
all 
@model$valid_class_error 
model$validation_metrics@$MSE 
all 
@model$varimp 
@model$_variable_importances 
all 
@model$confusion 
@model$training_metrics@metrics$cm$table 
binomial
and
multinomial 
@model$train_auc 
@model$train_AUC 
binomial 
@model$_validation_metrics 
all 

@model$_model_summary 
all 

@model$_scoring_history 
all 
Distributed Random Forest¶
Changes to DRF in H2O 3¶
Distributed Random Forest (DRF) was represented as h2o.randomForest(type="BigData", ...)
in H2O Classic. In H2O Classic, SpeeDRF (type="fast"
) was not as accurate, especially for complex data with categoricals, and did not address regression problems. DRF (type="BigData"
) was at least as accurate as SpeeDRF (type="fast"
) and was the only algorithm that scaled to big data (data too large to fit on a single node). In H2O 3, we improved the performance of DRF so that the data fits on a single node (optimally, for all cases), which will make SpeeDRF obsolete. Ultimately, the goal is provide a single algorithm that provides the “best of both worlds” for all datasets and use cases. Please note that H2O does not currently support the ability to specify the number of trees when using h2o.predict
for a DRF model.
Note: H2O 3 only supports DRF. SpeeDRF is no longer supported. The functionality of DRF in H2O 3 is similar to DRF functionality in H2O.
Renamed DRF Parameters¶
The following parameters have been renamed, but retain the same functions:
H2O Classic Parameter Name  H2O 3 Parameter Name 

data 
training_frame 
key 
model_id 
validation 
validation_frame 
sample.rate 
sample_rate 
ntree 
ntrees 
depth 
max_depth 
balance.classes 
balance_classes 
score.each.iteration 
score_each_iteration 
class.sampling.factors 
class_sampling_factors 
nodesize 
min_rows 
Deprecated DRF Parameters¶
The following parameters have been removed:
classification
: This is now automatically inferred from the response type. To achieve classification with a 0/1 response column, explicitly convert the response to a factor (as.factor()
).importance
: Variable importances are now computed automatically and displayed in the model output.holdout.fraction
: Specifying the fraction of the training data to hold out for validation is no longer supported.doGrpSplit
: The bitset group splitting of categorical variables is now the default.verbose
: Infonrmation about tree splits and extra statistics is now included automatically in the stdout.oobee
: The outofbag error estimate is now computed automatically (if no validation set is specified).stat.type
: This parameter was used for SpeeDRF, which is no longer supported.type
: This parameter was used for SpeeDRF, which is no longer supported.
New DRF Parameters¶
The following parameter has been added:
build_tree_one_node
: Run on a single node to use fewer CPUs.
DRF Algorithm Comparison¶
H2O Classic  H2O 3 

h2o.randomForest < function(x, 
h2o.randomForest < function( 
x, 
x, 
y, 
y, 
data, 
training_frame, 
key="", 
model_id, 
validation, 
validation_frame, 
mtries = 1, 
mtries = 1, 
sample.rate=2/3, 
sample_rate = 0.632, 
build_tree_one_node = FALSE, 

ntree=50 
ntrees=50, 
depth=20, 
max_depth = 20, 
min_rows = 1, 

nbins=20, 
nbins = 20, 
nbins_top_level, 

nbins_cats = 1024, 

binomial_double_trees = FALSE, 

balance.classes = FALSE, 
balance_classes = FALSE, 
seed = 1, 
seed 
nodesize = 1, 

classification = TRUE, 

importance=FALSE, 

weights_column = NULL, 

nfolds=0, 
nfolds = 0, 
fold_column = NULL, 

fold_assignment = c("AUTO",
"Random", "Module"), 

keep_cross_validation_predictions
= FALSE, 

score_each_iteration = FALSE, 

stopping_rounds = 0, 

stopping_metric = c("AUTO",
"deviance", "logloss", "MSE",
"AUC", "r2", "misclassification"), 

stopping_tolerance = 0.001) 

holdout.fraction = 0, 

max.after.balance.size = 5, 
max_after_balance_size, 
class.sampling.factors = NULL, 

doGrpSplit = TRUE, 

verbose = FALSE, 

oobee = TRUE, 

stat.type = "ENTROPY," 

type = "fast") 
Output¶
The following table provides the component name in H2O, the
corresponding component name in H2O 3 (if supported), and the model
type (binomial, multinomial, or all). Many components are now included
in h2o.performance
; for more information, refer to
h2o.performance.
H2O Classic  H2O 3  Model Type 

@model$priorDistribution 
all 

@model$params 
@allparameters 
all 
@model$mse 
@model$scoring_history 
all 
@model$forest 
@model$model_summary 
all 
@model$classification 
all 

@model$varimp 
@model$variable_importances 
all 
@model$confusion 
@model$training_metrics@metrics$cm$table 
binomial
and
multinomial 
@model$auc 
@model$training_metrics@metrics$AUC 
binomial 
@model$gini 
@model$training_metrics@metrics$Gini 
binomial 
@model$best_cutoff 
binomial 

@model$F1 
@model$training_metrics@metrics$thresholds_and_metric_scores$f1 
binomial 
@model$F2 
@model$training_metrics@metrics$thresholds_and_metric_scores$f2 
binomial 
@model$accuracy 
@model$training_metrics@metrics$thresholds_and_metric_scores$accuracy 
binomial 
@model$Error 
@model$Error 
binomial 
@model$precision 
model$training_metrics@metrics$thresholds_and_metric_scores$precision 
binomial 
@model$recall 
model$training_metrics@metrics$thresholds_and_metric_scores$recall 
binomial 
@model$mcc 
model$training_metrics@metrics$thresholds_and_metric_scores$absolute_MCC 
binomial 
@model$max_per_class_err 
currently replaced by
@model$training_metrics@metrics$thresholds_and_metric_scores$min_per_class_correct 
binomial 