classifier.fit(X_train, y_train) #Evaluate the model: print("training set score: %f" % classifier.score(X_train, y_train)) print("test set score: %f" % classifier.score(X_test, y_test)) 下面使用混淆矩阵来可视化结果: # Predicting the Test set results y_pred = classifier.predict(X_...
上面训练时,没有设置拉普拉斯估计,此处设为1,性能有所提升。 ## Step 5: Improving model performance ---sms_classifier2<-naiveBayes(sms_train,sms_train_labels,laplace=1)#拉普拉斯估计值sms_test_pred2<-predict(sms_classifier2,sms_test)CrossTable(sms_test_pred2,sms_test_labels,prop.chisq=FALSE,pr...
library(e1071) sms_classifier <- naiveBayes(sms_train, sms_train_labels) 4)评估模型性能 基于测试集中的未知短信来检验分类器的预测值。比较预测值和真实值,仍然通过混淆矩阵来计算。 ## Step 4: Evaluating model performance --- sms_test_pred <- predict(sms_classifier, sms_test) library(gmodels) ...
上面训练时,没有设置拉普拉斯估计,此处设为1,性能有所提升。 ## Step 5: Improving model performance ---sms_classifier2<-naiveBayes(sms_train,sms_train_labels,laplace=1)#拉普拉斯估计值sms_test_pred2<-predict(sms_classifier2,sms_test)CrossTable(sms_test_pred2,sms_test_labels,prop.chisq=FALSE,pr...
>>> print(clf.predict(X[2:3])) [3] Notes --- For the rationale behind the names `coef_` and `intercept_`, i.e. naive Bayes as a linear classifier, see J. Rennie et al. (2003), Tackling the poor assumptions of naive Bayes text classifiers, ICML. References...
sms_classifier <- naiveBayes(sms_train, sms_train_labels) 1. 2. 3. 4)评估模型性能 基于测试集中的未知短信来检验分类器的预测值。比较预测值和真实值,仍然通过混淆矩阵来计算。 ## Step 4: Evaluating model performance --- sms_test_pred <- predict(sms_classifier, sms_test) library...
设计思路 核心代码 vec = CountVectorizer() X_train = vec.fit_transform(X_train) X_test = vec.transform(X_test) mnb = MultinomialNB() mnb.fit(X_train, y_train) y_predict = mnb.predict(X_test) print('The accuracy of Naive Bayes Classifier is', mnb.score(X_test, y_test))...
sms_classifier <- naiveBayes(sms_train, sms_raw_train$type) sms_classifier ## 第四步: 评估模型性能 sms_test_pred <- predict(sms_classifier, sms_test) library(gmodels) CrossTable(sms_test_pred, sms_raw_test$type, prop.chisq = TRUE, prop.t = TRUE, prop.r = TRUE, dnn = c('predi...
Furthermore, we use machine learning algorithms for training classifiers to predict protein-RNA interfaces using information derived from the sequence and structural features. We develop the Struct-NB classifier that takes into account structural information. We compare the performance of Nave Bayes and ...
rf = RandomForestClassifier rf = rf.fit(X_train, y_train) y_pred = rf.predict(X_test) skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True) plt.show scikitplot.metrics.plot_roc 快速展示模型预测的每个类别的ROC曲线。