# col_sample_rate_per_tree¶

• Available in: GBM, DRF
• Hyperparameter: yes

## Description¶

This option specifies the column sampling rate for each tree. This can be a value from 0.0 to 1.0. Note that it is multiplicative with col_sample_rate, so setting both parameters to 0.8, for example, results in 64% of columns being considered at any given node to split.

For an example model using:

• 100-column dataset
• col_sample_rate_per_tree=0.754
• col_sample_rate=0.8 (Samples 80% of columns per split)

For each tree, the floor is used to determine the number of columns - in this example, (0.754 * 100)=75 out of 100 - that are randomly picked, and then the floor is used to determine the number of columns - in this case, (0.754 * 0.8 * 100)=60 - that are then randomly chosen for each split decision (out of the 75).

## Example¶

library(h2o)
h2o.init()
# import the airlines dataset:
# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
# original data can be found at http://www.transtats.bts.gov/

# convert columns to factors
airlines["Year"] <- as.factor(airlines["Year"])
airlines["Month"] <- as.factor(airlines["Month"])
airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"])
airlines["Cancelled"] <- as.factor(airlines["Cancelled"])
airlines['FlightNum'] <- as.factor(airlines['FlightNum'])

# set the predictor names and the response column name
predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum")
response <- "IsDepDelayed"

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

# try using the col_sample_rate_per_tree parameter:
airlines.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
validation_frame = valid, col_sample_rate_per_tree =.7 ,
seed = 1234)

# print the AUC for the validation data
print(h2o.auc(airlines.gbm, valid = TRUE))

# Example of values to grid over for col_sample_rate_per_tree
hyper_params <- list( col_sample_rate_per_tree = c(.3, .7, .8, 1) )

# 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")
# this GBM uses early stopping once the validation AUC doesn't improve by at least 0.01% for
# 5 consecutive scoring events
grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid,
algorithm = "gbm", grid_id = "air_grid", hyper_params = hyper_params,
stopping_rounds = 5, stopping_tolerance = 1e-4, stopping_metric = "AUC",
search_criteria = list(strategy = "Cartesian"), seed = 1234)

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

import h2o
h2o.init()

# import the airlines dataset:
# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
# original data can be found at http://www.transtats.bts.gov/

# convert columns to factors
airlines["Year"]= airlines["Year"].asfactor()
airlines["Month"]= airlines["Month"].asfactor()
airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
airlines["Cancelled"] = airlines["Cancelled"].asfactor()
airlines['FlightNum'] = airlines['FlightNum'].asfactor()

# set the predictor names and the response column name
predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"]
response = "IsDepDelayed"

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

# try using the col_sample_rate_per_tree parameter:
airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate_per_tree = .7, seed =1234)

airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the auc for the validation data
print(airlines_gbm.auc(valid=True))

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

# select the values for col_sample_rate_per_tree to grid over
hyper_params = {'col_sample_rate_per_tree': [.3, .7, .8, 1]}

# 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
# use early stopping once the validation AUC doesn't improve by at least 0.01% for
# 5 consecutive scoring events
stopping_rounds = 5,
stopping_metric = "AUC", stopping_tolerance = 1e-4)

# build grid search with previously made GBM and hyper parameters
grid = H2OGridSearch(model = airlines_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)