"NaiveBayes": GaussianNB(), "KNN": KNeighborsClassifier(n_neighbors=5) } # 结果存储字典 results = {model: {}formodelinmodels.keys()} 03 批量训练与评价 进行模型批量训练、预测与评价。 # 批量训练与预测 thresholds = ...
deffit(self,xTrain,yTrain=pd.Series()):ifnot yTrain.empty:#如果不传,自动选择最后一列作为分类标签 xTrain=pd.concat([xTrain,yTrain],axis=1)self.model=self.buildNaiveBayes(xTrain)returnself.model defbuildNaiveBayes(self,xTrain):yTrain=xTrain.iloc[:,-1]yTrainCounts=yTrain.value_counts()...
defCalXMean(X_byclass):###计算各类别特征各维度的平均值###输入当前类别下的特征,输出该特征各个维度的平均值###X_mean=[]foriinrange(X_byclass.shape[1]): X_mean.append(np.mean(X_byclass[:,i]))returnX_meandefCalXVar(X_byclass):###计算各类别特征各维度的方差###输入当前类别下的特征,...
data[:,-1]X,y=create_data()X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)model=NaiveBayes()model.fit(X_train,y_train)print(model.predict([4.4,3.2,1.3,0.2]))print(model.score(X_test,y_test)
Real time Prediction:Naive Bayes is an eager learning classifier and it is sure fast. Thus, it could be used for making predictions in real time. Multi class Prediction:This algorithm is also well known for multi class prediction feature. Here we can predict the probability of multiple classes...
Real time Prediction:Naive Bayes is an eager learning classifier and it is sure fast. Thus, it could be used for making predictions in real time. Multi class Prediction:This algorithm is also well known for multi class prediction feature. Here we can predict the probability of multiple classes...
Naive Bayes is a simple and easy to implement algorithm. Because of this, it might outperform more complex models when the amount of data is limited. Naive Bayes works well with numerical and categorical data. It can also be used to perform regression by using Gaussian Naive Bayes. ...
1.Naïve Bayes Classifier: Naïve Bayes is a supervised machine learning algorithm used for classification problems. It is built on Bayes Theorem. It is called Naïve because of its Naïve assumption of Conditional Independence among predictors. ...
朴素贝叶斯模型(Naive Bayes Model, NBM)是一种基于贝叶斯定理和特征条件独立性假设的分类算法。其核心思想是通过给定特征X的条件下,预测样本属于某类别c的后验概率P(c|X),选择后验概率最大的类别作为分类结果。 基本原理 朴素贝叶斯模型的基本原理基于贝叶斯定理,公式如下: ...
#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm from sklearn.ensemble import RandomForestClassifier forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) ...