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model_parameter_settings.txt
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model_parameter_settings.txt
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random_state_new = 969
sklearn = 1.1.1
xgboost = 1.5.2
numpy = 1.20.3
pandas = 1.4.1
seaborn = 0.11.2
matplotlib = 3.5.1
LR
LogisticRegression(
penalty='l2',
*,
dual=False,
tol=0.0001,
C=1.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=None,
solver='liblinear',
max_iter=100,
multi_class='auto',
verbose=0,
warm_start=False,
n_jobs=None,
l1_ratio=None,
)
GBM
GradientBoostingClassifier(
*,
loss="log_loss",
learning_rate=0.1,
n_estimators=100,
subsample=1.0,
criterion="friedman_mse",
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_depth=3,
min_impurity_decrease=0.0,
init=None,
random_state=random_state_new,
max_features=None,
verbose=0,
max_leaf_nodes=None,
warm_start=False,
validation_fraction=0.1,
n_iter_no_change=None,
tol=1e-4,
ccp_alpha=0.0
)
XGB
XGBClassifier(
base_score=0.5,
booster='gbtree',
colsample_bylevel=1,
colsample_bynode=1,
colsample_bytree=1,
gamma=0,
gpu_id=-1,
importance_type='gain',
interaction_constraints='',
learning_rate=0.300000012,
max_delta_step=0,
max_depth=2,
min_child_weight=1,
missing=nan,
monotone_constraints='()',
n_estimators=100,
n_jobs=8,
num_parallel_tree=1,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
subsample=1,
tree_method='exact',
validate_parameters=1,
verbosity=None
)
RF
RandomForestClassifier(
n_estimators=10,
*,
criterion="gini",
max_depth=3,
min_samples_split=12,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features="sqrt",
max_leaf_nodes=None,
min_impurity_decrease=0.0,
bootstrap=True,
oob_score=False,
n_jobs=None,
random_state=random_state_new,
verbose=0,
warm_start=False,
class_weight=None,
ccp_alpha=0.0,
max_samples=None
)
NB
GaussianNB(*,
priors=None,
var_smoothing=1e-9
)
DT
DecisionTreeClassifier(*,
criterion="gini",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.25,
max_features=None,
random_state=random_state_new,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
class_weight=None,
ccp_alpha=0.0
)