from sklearn.naive_bayes import GaussianNB # Build a Gaussian Classifier model = GaussianNB() # Model training model.fit(X_train, y_train) # Predict Output predicted = model.predict([X_test[6]]) print("Actual Value:", y_test[6]) print("Predicted Value:", predicted[0]) Powered By ...
可以创建一个管道,将TF–IDF 向量化方法与多项式朴素贝叶斯分类器组合在一起: from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.pipeline import make_pipelinemodel = make_pipeline(TfidfVectorizer(), MultinomialNB()) 1. 通过这个管道,就可以将...
示例1: init_classifier_impl ▲点赞 5▼ # 需要导入模块: import sklearn [as 别名]# 或者: from sklearn importnaive_bayes[as 别名]definit_classifier_impl(field_code: str, init_script: str):ifinit_scriptisnotNone: init_script = init_script.strip()ifnotinit_script:fromsklearnimporttreeasskl...
Just so you know what you are getting into, this is along storythat contains a mathematical explanation of the Naive Bayes classifier with 6 different Python examples. Please take a look at thelist of topics belowand feel free to jump to the most interesting sections for you. Intro Machine ...
from sklearn.naive_bayes import GaussianNB #Calling the Class naive_bayes = GaussianNB() #Fitting the data to the classifier naive_bayes.fit(X_train , y_train) #Predict on test data y_predicted = naive_bayes.predict(X_test) The.fitmethod ofGaussianNBclass requires the feature data (X_trai...
In this step, we will use the test data to evaluate the trained model accuracy. Again, sklearn made it extremely easy. All we need to do is to call thescoremethod on our classifier. Following is the code: Python fromsklearn.naive_bayesimportMultinomialNB ...
from sklearn.naive_bayes import GaussianNB # Build a Gaussian Classifier model = GaussianNB() # Model training model.fit(X_train, y_train) # Predict Output predicted = model.predict([X_test[6]]) print("Actual Value:", y_test[6]) ...
NAIVE_BAYES: self._clf = GaussianNB() elif classifier == FaceClassifierModels.RBF_SVM: self._clf = SVC(C=1, kernel='rbf', probability=True, gamma=2) elif classifier == FaceClassifierModels.NEAREST_NEIGHBORS: self._clf = KNeighborsClassifier(1) elif classifier == FaceClassifierModels....
# 需要導入模塊: from sklearn import naive_bayes [as 別名]# 或者: from sklearn.naive_bayes importBernoulliNB[as 別名]deftest_export_random_ind():"""Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""tpot_obj = TPOTClassifier(random_state=39,...
defclassify(features_train, labels_train):### import the sklearn module for GaussianNB### create classifier### fit the classifier on the training features and labels### return the fit classifierfromsklearn.naive_bayesimportGaussianNB clf=GaussianNB() ...