9954
7096
cv
null
7096
error_score
"raise"
7096
fit_params
{}
7096
iid
true
7096
n_iter
50
7096
n_jobs
-1
7096
param_distributions
{"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__bootstrap": [true, false], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
7096
pre_dispatch
"2*n_jobs"
7096
random_state
1
7096
refit
true
7096
return_train_score
true
7096
scoring
null
7096
verbose
0
7096
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"median"
6947
strategy_nominal
"most_frequent"
6947
verbose
0
6947
categorical_features
[]
6948
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
6948
handle_unknown
"ignore"
6948
n_values
"auto"
6948
sparse
true
6948
threshold
0.0
6949
bootstrap
true
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.2
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
1
6902
min_samples_split
2
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
10
6902
n_jobs
1
6902
oob_score
false
6902
random_state
1
6902
verbose
0
6902
warm_start
false
6902
openml-python
Sklearn_0.18.1.
predictive_accuracy
0.725
predictive_accuracy
0.725
predictive_accuracy
0.675
predictive_accuracy
0.6875
predictive_accuracy
0.73125
predictive_accuracy
0.71875
predictive_accuracy
0.71875
predictive_accuracy
0.7625
predictive_accuracy
0.65625
predictive_accuracy
0.73125