mtries

• Available in: DRF
• Hyperparameter: yes

Description

Use this option to specify the number of columns to randomly select at each level.

This value defaults to -1, and the range for this option is -1 to >=1. If the default value is used, then the number of variables is:

• the square root of the number of columns for classification
• p/3 for regression (where p is the number of predictors).

The following illustrates how column sampling is implemented for DRF. For an example model using:

• 100 columns
• col_sample_rate_per_tree is 0.602
• mtries is -1 or 7 (refers to the number of active predictor columns for the dataset)

For each tree, the floor is used to determine the number of columns that are randomly picked (for this example, (0.602*100)=60 out of the 100 columns).

For classification cases where mtries=-1, the square root is randomly chosen for each split decision (out of the total 60 - for this example, (\(\sqrt{100}\) = 10 columns).

For regression, the floor is used for each split by default (in this example, (100/3)=33 columns). If mtries=7, then 7 columns are picked for each split decision (out of the 60).

mtries is configured independently of col_sample_rate_per_tree, but it can be limited by it. For example, if col_sample_rate_per_tree=0.01, then there’s only one column left for each split, regardless of how large the value for mtries is.

Related Parameters

• col_sample_rate_change_per_level
• col_sample_rate_per_tree

Example

library(h2o)
h2o.init()

# import the covtype dataset:
# this dataset is used to classify the correct forest cover type
# original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Covertype
covtype <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")

# convert response column to a factor
covtype[,55] <- as.factor(covtype[,55])

# set the predictor names and the response column name
predictors <- colnames(covtype[1:54])
response <- 'C55'

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

# try using the `mtries` parameter:
cov_drf <- h2o.randomForest(x = predictors, y = response, training_frame = train,
                   validation_frame = valid, mtries = 30, seed = 1234)
print(h2o.logloss(cov_drf, valid = TRUE))

# grid over `mtries` parameter:
# select the values for `mtries` to grid over
hyper_params <- list( mtries = c(10, 40, 50) )

# 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"}

# build grid search with previously made DRF and hyperparameters
grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid,
                 algorithm = "drf", grid_id = "covtype_grid", hyper_params = hyper_params,
                 search_criteria = list(strategy = "Cartesian"), seed = 1234)

# Sort the grid models by logloss
sortedGrid <- h2o.getGrid("covtype_grid", sort_by = "logloss", decreasing = FALSE)
sortedGrid

import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
h2o.init()

# import the covtype dataset:
# this dataset is used to classify the correct forest cover type
# original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Covertype
covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")

# convert response column to a factor
covtype[54] = covtype[54].asfactor()

# set the predictor names and the response column name
predictors = covtype.columns[0:54]
response = 'C55'

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

# try using the `mtries` parameter:
cov_drf = H2ORandomForestEstimator(mtries = 30, seed = 1234)
cov_drf.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

print('logloss', cov_drf.logloss(valid = True))

# grid over `mtries` parameter:
# import Grid Search
from h2o.grid.grid_search import H2OGridSearch

# select the values for `mtries` to grid over
hyper_params = {'mtries': [10, 40, 50]}

# 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 drf estimator
cov_drf_2 = H2ORandomForestEstimator(seed = 1234)

# build grid search with previously made DRF and hyperparameters
grid = H2OGridSearch(model = cov_drf_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 logloss
sorted_grid = grid.get_grid(sort_by='logloss', decreasing=False)
print(sorted_grid)