`ntrees`¶

Available in: GBM, DRF, XGBoost, Isolation Forest
Hyperparameter: yes

Description¶

For tree-based algorithms, this option specifies the number of trees to build in the model. In tree-based models, each node in the tree corresponds to a feature field from a dataset. Except for the top node, each node has an incoming branch. Similarly, except for the bottom node (or leaf node), each node has a number of outgoing branches. A branch represents a possible value for the input field from the originating dataset. A leaf represents the value of the objective field, given all the values for each input field in the chain of branches that go from the root (top) to that leaf.

This option defaults to 50 trees.

Example¶

library(h2o)
h2o.init()
# import the titanic dataset:
# This dataset is used to classify whether a passenger will survive '1' or not '0'
# original dataset can be found at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html
titanic <-  h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")

# convert response column to a factor
titanic['survived'] <- as.factor(titanic['survived'])

# set the predictor names and the response column name
# predictors include all columns except 'name' and the response column ("survived")
predictors <- setdiff(colnames(titanic), colnames(titanic)[2:3])
response <- "survived"

# split into train and validation
titanic.splits <- h2o.splitFrame(data =  titanic, ratios = .8, seed = 1234)
train <- titanic.splits[[1]]
valid <- titanic.splits[[2]]

# try a range of ntrees:
bin_num = c(20, 50, 80, 110, 140, 170, 200)
label = c("20", "50", "80", "110", "140", "170", "200")
lapply(seq_along(1:length(bin_num)),function(num) {
  titanic.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid,
                          ntrees = bin_num[num], nfolds = 5, seed = 1234)
  # print the value used and AUC score for train and valid
  print(paste(label[num], 'training score',  h2o.auc(titanic.gbm, train = TRUE)))
  print(paste(label[num], 'validation score',  h2o.auc(titanic.gbm, valid = TRUE)))
})


# Example of values to grid over for `ntrees`
hyper_params <- list( ntrees = c(20, 50, 80, 110, 140, 170, 200) )

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: list(strategy = "RandomDiscrete")
grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid,
                 algorithm = "gbm", grid_id = "titanic_grid", hyper_params = hyper_params,
                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

## Sort the grid models by AUC
sortedGrid <- h2o.getGrid("titanic_grid", sort_by = "auc", decreasing = TRUE)
sortedGrid

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o.cluster().show_status()

# import the titanic dataset:
# This dataset is used to classify whether a passenger will survive '1' or not '0'
# original dataset can be found at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html
titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")

# convert response column to a factor
titanic['survived'] = titanic['survived'].asfactor()

# set the predictor names and the response column name
# predictors include all columns except 'name' and the response column ("survived")
predictors = titanic.columns
del predictors[1:3]
response = 'survived'

# split into train and validation sets
train, valid = titanic.split_frame(ratios = [.8], seed = 1234)

# try a range of values for `ntrees`
tree_num = [20, 50, 80, 110, 140, 170, 200]
label = ["20", "50", "80", "110", "140", "170", "200"]
for key, num in enumerate(tree_num):
    # initialize the GBM estimator and set a seed for reproducibility
    titanic_gbm = H2OGradientBoostingEstimator(ntrees = num, seed = 1234)
    titanic_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)
    # print the value and AUC score for train and validation sets
    print(label[key], 'training score', titanic_gbm.auc(train = True))
    print(label[key], 'validation score', titanic_gbm.auc(valid = True))


# Example of values to grid over for `ntrees`
# import Grid Search
from h2o.grid.grid_search import H2OGridSearch

# select the values for `ntrees` to grid over
hyper_params = {'ntrees': [20, 50, 80, 110, 140, 170, 200]}

# this example uses cartesian grid search because the search space is small
# and we want to see the performance of all models. For a larger search space use
# random grid search instead: {'strategy': "RandomDiscrete"}
# initialize the GBM estimator
tree_gbm_2 = H2OGradientBoostingEstimator(seed = 1234)

# build grid search with previously made GBM and hyper parameters
grid = H2OGridSearch(model = tree_gbm_2, hyper_params = hyper_params,
                     search_criteria = {'strategy': "Cartesian"})

# train using the grid
grid.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# sort the grid models by decreasing AUC
sorted_grid = grid.get_grid(sort_by='auc', decreasing=True)
print(sorted_grid)

ntrees¶

Description¶

Example¶

`ntrees`¶