Python RandomForestClassifier._more_tags Exemples

Langage de programmation: Python

Espace de nommage/Pack: sklearn.ensemble

Méthode/Fonction: _more_tags

Exemples au hotexamples.com: 1

Python RandomForestClassifier._more_tags - 1 exemples trouvés. Ce sont les exemples réels les mieux notés de sklearn.ensemble.RandomForestClassifier._more_tags extraits de projets open source. Vous pouvez noter les exemples pour nous aider à en améliorer la qualité.

Méthodes fréquemment utilisées

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add(30)

fit(30)

RandomForestClassifier(30)

apply(30)

get_params(30)

decision_path(24)

compile(22)

decision_function(17)

fit_transform(12)

n_estimators(12)

n_classes_(12)

evaluate(11)

__init__(11)

classes_(10)

estimators_(7)

max_depth(4)

error(3)

min_samples_leaf(3)

class_weight(3)

lower(3)

eval(2)

input_features(2)

expand(2)

id(2)

encode(2)

feature_importances_(2)

feature_names(2)

fit_predict(2)

C(2)

criterion(2)

max_features(2)

mlinit(2)

compress(2)

Predict(2)

base_estimator(2)

append(2)

output_feature(2)

_get_param_names(2)

get_variable_value(1)

variable_feature_importances(1)

values(1)

get_weights(1)

get_xgb_params(1)

predicted_proba(1)

name1(1)

k_best(1)

min_weight_fraction_leaf(1)

make_classification(1)

kneighbors(1)

min_samples_split(1)

Méthodes fréquemment utilisées

add (30)

fit (30)

RandomForestClassifier (30)

apply (30)

get_params (30)

decision_path (24)

compile (22)

decision_function (17)

fit_transform (12)

n_estimators (12)

Méthodes fréquemment utilisées

n_classes_ (12)

evaluate (11)

__init__ (11)

classes_ (10)

estimators_ (7)

max_depth (4)

error (3)

min_samples_leaf (3)

class_weight (3)

lower (3)

eval (2)

input_features (2)

expand (2)

id (2)

encode (2)

feature_importances_ (2)

feature_names (2)

fit_predict (2)

C (2)

criterion (2)

Méthodes fréquemment utilisées

eval (2)

input_features (2)

expand (2)

id (2)

encode (2)

feature_importances_ (2)

feature_names (2)

fit_predict (2)

C (2)

criterion (2)

max_features (2)

mlinit (2)

compress (2)

Predict (2)

base_estimator (2)

append (2)

output_feature (2)

_get_param_names (2)

get_variable_value (1)

variable_feature_importances (1)

values (1)

get_weights (1)

get_xgb_params (1)

predicted_proba (1)

name1 (1)

k_best (1)

min_weight_fraction_leaf (1)

make_classification (1)

kneighbors (1)

min_samples_split (1)

Méthodes fréquemment utilisées

max_features (2)

mlinit (2)

compress (2)

Predict (2)

base_estimator (2)

append (2)

output_feature (2)

_get_param_names (2)

get_variable_value (1)

variable_feature_importances (1)

values (1)

get_weights (1)

get_xgb_params (1)

predicted_proba (1)

name1 (1)

k_best (1)

min_weight_fraction_leaf (1)

make_classification (1)

kneighbors (1)

min_samples_split (1)

label_identification_ (1)

min_impurity_split (1)

label_long_names_ (1)

lda (1)

min_impurity_decrease (1)

make_Labels_From_Dataset (1)

max_leaf_nodes (1)

get_variable_names (1)

evt_predict (1)

get_fscore (1)

cost_complexity_pruning_path (1)

PlotParamTrace (1)

PlotPredictiveDistribution (1)

SamplePosterior (1)

SelectFromModel (1)

__dict__ (1)

__repr__ (1)

_more_tags (1)

_name (1)

_predict_proba_lr (1)

Exemple #1

0

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Fichier : forest.py Projet : iamDecode/sklearn-pmml-model

def _more_tags(self): return RandomForestClassifier._more_tags(self)