How to create conda environment for benchmarking pip install -r xgboost_bench/requirements.txt
# or You can launch benchmarks for each algorithm separately. The table below lists all supported parameters for each algorithm.
parameter Name
Type
default value
description
num-threads
int
-1
The number of threads to use
arch
str
?
Achine architecture, for bookkeeping
batch
str
?
Batch ID, for bookkeeping
prefix
str
sklearn
Prefix string, for bookkeeping
header
action
False
Output CSV header
verbose
action
False
Output extra debug messages
data-format
str
numpy
Data formats: numpy , pandas or cudf
data-order
str
C
Data order: C (row-major, default) or F (column-major)
dtype
np.dtype
np.float64
Data type: float64 (default) or float32
check-finiteness
action
False
Check finiteness in sklearn input check(disabled by default)
output-format
str
csv
Output format: csv (default) or json '
time-method
str
mean_min
Method used for time mesurements
box-filter-measurements
int
100
Maximum number of measurements in box filter
inner-loops
int
100
Maximum inner loop iterations. (we take the mean over inner iterations)
outer-loops
int
100
Maximum outer loop iterations. (we take the min over outer iterations)
time-limit
float
10
Target time to spend to benchmark
goal-outer-loops
int
10
The number of outer loops to aim while automatically picking number of inner loops. If zero, do not automatically decide number of inner loops.
seed
int
12345
Seed to pass as random_state
dataset-name
str
None
Dataset name
parameter Name
Type
default value
description
n-estimators
int
100
The number of gradient boosted trees
learning-rate
float
0.3
Step size shrinkage used in update to prevents overfitting
min-split-loss
float
0
Minimum loss reduction required to make partition on a leaf node
max-depth
int
6
Maximum depth of a tree
min-child-weight
float
1
Minimum sum of instance weight needed in a child
max-delta-step
float
0
Maximum delta step we allow each leaf output to be
subsample
float
1
Subsample ratio of the training instances
colsample-bytree
float
1
Subsample ratio of columns when constructing each tree
reg-lambda
float
1
L2 regularization term on weights
reg-alpha
float
0
L1 regularization term on weights
tree-method
str
The tree construction algorithm used in XGBoost
scale-pos-weight
float
1
Controls a balance of positive and negative weights
grow-policy
str
depthwise
Controls a way new nodes are added to the tree
max-leaves
int
0
Maximum number of nodes to be added
max-bin
int
256
Maximum number of discrete bins to bucket continuous features
objective
str
True
reg:squarederror , binary:logistic , multi:softmax or multi:softprob . Control a balance of positive and negative weights