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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
box_filter or 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
DBSCAN
parameter Name
Type
default value
description
epsilon
float
10
Radius of neighborhood of a point
min_samples
int
5
The minimum number of samples required in a 'neighborhood to consider a point a core point
RandomForestClassifier
parameter Name
Type
default value
description
criterion
str
gini
gini or entropy. The function to measure the quality of a split
num-trees
int
100
The number of trees in the forest
max-features
float_or_int
None
Upper bound on features used at each split
max-depth
int
None
Upper bound on depth of constructed trees
min-samples-split
float_or_int
2
Minimum samples number for node splitting
max-leaf-nodes
int
None
Maximum leaf nodes per tree
min-impurity-decrease
float
0
Needed impurity decrease for node splitting
no-bootstrap
store_false
True
Don't control bootstraping
RandomForestRegressor
parameter Name
Type
default value
description
criterion
str
gini
gini or entropy. The function to measure the quality of a split
num-trees
int
100
The number of trees in the forest
max-features
float_or_int
None
Upper bound on features used at each split
max-depth
int
None
Upper bound on depth of constructed trees
min-samples-split
float_or_int
2
Minimum samples number for node splitting
max-leaf-nodes
int
None
Maximum leaf nodes per tree
min-impurity-decrease
float
0
Needed impurity decrease for node splitting
no-bootstrap
action
True
Don't control bootstraping
use-sklearn-class
action
Force use of sklearn.ensemble.RandomForestClassifier
pairwise_distances
parameter Name
Type
default value
description
metric
str
cosine
cosine or correlation Metric to test for pairwise distances
KMeans
parameter Name
Type
default value
description
init
str
Initial clusters
tol
float
0
Absolute threshold
maxiter
inte
100
Maximum number of iterations
n-clusters
int
The number of clusters
KNeighborsClassifier
parameter Name
Type
default value
description
n-neighbors
int
5
The number of neighbors to use
weights
str
uniform
Weight function used in prediction
method
str
brute
Algorithm used to compute the nearest neighbors
metric
str
euclidean
Distance metric to use
LinearRegression
parameter Name
Type
default value
description
no-fit-intercept
action
True
Don't fit intercept (assume data already centered)
LogisticRegression
parameter Name
Type
default value
description
no-fit-intercept
action
True
Don't fit intercept
multiclass
str
auto
auto, ovr or multinomial. How to treat multi class data
solver
str
lbfgs
lbfgs, newton-cg or saga. Solver to use
maxiter
int
100
Maximum iterations for the iterative solver
C
float
1.0
Regularization parameter
tol
float
None
Tolerance for solver
PCA
parameter Name
Type
default value
description
svd-solver
str
daal
daal, full. SVD solver to use
n-components
int
None
The number of components to find
whiten
action
False
Perform whitening
Ridge
parameter Name
Type
default value
description
no-fit-intercept
action
True
Don't fit intercept (assume data already centered)
solver
str
auto
Solver used for training
alpha
float
1.0
Regularization strength
SVC
parameter Name
Type
default value
description
C
float
0.01
SVM slack parameter
kernel
str
linear
linear, rbf, or poly. SVM kernel function
gamma
float
None
Parameter for kernel="rbf"
max-cache-size
int
64
Maximum cache size for SVM.
tol
float
1e-16
Tolerance passed to sklearn.svm.SVC
probability
action
True
Use probability for SVC
train_test_split
parameter Name
Type
default value
description
train-size
float
0.75
Size of training subset
test-size
float
0.25
Size of testing subset
do-not-shuffle
action
False
Do not perform data shuffle before splitting
include-y
action
False
Include label (Y) in splitting
rng
str
None
MT19937, SFMT19937, MT2203, R250, WH, MCG31, MCG59, MRG32K3A, PHILOX4X32X10, NONDETERM or None. Random numbers generator for shuffling.(only for IDP scikit-learn)