本文整理匯總了Python中sklearn.naive_bayes方法的典型用法代碼示例。如果您正苦於以下問題：Python sklearn.naive_bayes方法的具體用法？Python sklearn.naive_bayes怎麽用？Python sklearn.naive_bayes使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn的用法示例。
在下文中一共展示了sklearn.naive_bayes方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: init_classifier_impl
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import naive_bayes [as 別名]
def init_classifier_impl(field_code: str, init_script: str):
if init_script is not None:
init_script = init_script.strip()
if not init_script:
from sklearn import tree as sklearn_tree
return sklearn_tree.DecisionTreeClassifier()
from sklearn import tree as sklearn_tree
from sklearn import neural_network as sklearn_neural_network
from sklearn import neighbors as sklearn_neighbors
from sklearn import svm as sklearn_svm
from sklearn import gaussian_process as sklearn_gaussian_process
from sklearn.gaussian_process import kernels as sklearn_gaussian_process_kernels
from sklearn import ensemble as sklearn_ensemble
from sklearn import naive_bayes as sklearn_naive_bayes
from sklearn import discriminant_analysis as sklearn_discriminant_analysis
from sklearn import linear_model as sklearn_linear_model
eval_locals = {
'sklearn_linear_model': sklearn_linear_model,
'sklearn_tree': sklearn_tree,
'sklearn_neural_network': sklearn_neural_network,
'sklearn_neighbors': sklearn_neighbors,
'sklearn_svm': sklearn_svm,
'sklearn_gaussian_process': sklearn_gaussian_process,
'sklearn_gaussian_process_kernels': sklearn_gaussian_process_kernels,
'sklearn_ensemble': sklearn_ensemble,
'sklearn_naive_bayes': sklearn_naive_bayes,
'sklearn_discriminant_analysis': sklearn_discriminant_analysis
}
return eval_script('classifier init script of field {0}'.format(field_code), init_script, eval_locals) 示例2: selection_parameters_for_classfier
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import naive_bayes [as 別名]
def selection_parameters_for_classfier(X,y):
from sklearn import grid_search
#paras={ 'n_neighbors':[1,10], 'weights':['uniform', 'distance'], 'algorithm':['auto', 'ball_tree','kd_tree', 'brute'], 'leaf_size':[20,50]}
#knn = KNeighborsClassifier()
#naive_bayes
#nbg = GaussianNB()
#nbm = MultinomialNB()
#nbb = BernoulliNB()
#decision tree
#paras={ 'criterion':['gini','entropy'], 'splitter':['random', 'best'], 'max_features':[None, 'auto','sqrt', 'log2'], 'min_samples_split':[1,10]}
#dtree = DecisionTreeClassifier()
#random forest
#rforest = RandomForestClassifier()
#paras={ 'n_estimators':[2,15], 'criterion':['gini','entropy'], 'max_features': ['auto','sqrt', 'log2'], 'min_samples_split':[1,10]}
#svm
svmm = svm.SVC()
paras={'kernel':['rbf','linear','poly']}
clt =grid_search.GridSearchCV(svmm, paras, cv=5)
clt.fit(X,y)
print (clt)
#print (clt.get_params())
print (clt.set_params())
print (clt.score(X,y))
#scores = cross_val_score(clt,X,y,cv=10)
#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
#this is to get score using cross_validation