首页from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC, LinearSVC from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import Perceptron from sklearn.linear_model import SGDClassifier from sklearn.tree import DecisionTreeClassifier，将以上代码编写成R语言代码

from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC, LinearSVC from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import Perceptron from sklearn.linear_model import SGDClassifier from sklearn.tree import DecisionTreeClassifier，将以上代码编写成R语言代码

时间: 2023-11-20 20:04:46 浏览: 185

好的，以下是将上述Python代码转化为R语言代码的结果： ```R # 导入必要的库 library(caret) library(randomForest) library(e1071) library(class) library(naivebayes) library(nnet) library(rpart) # 定义模型 logreg_model <- train(Survived ~ ., data = train_data, method = "glm", family = "binomial") svm_model <- train(Survived ~ ., data = train_data, method = "svmRadial") rf_model <- train(Survived ~ ., data = train_data, method = "rf") knn_model <- train(Survived ~ ., data = train_data, method = "knn") nb_model <- train(Survived ~ ., data = train_data, method = "nb") nn_model <- train(Survived ~ ., data = train_data, method = "nnet") dt_model <- train(Survived ~ ., data = train_data, method = "rpart") # 对测试集进行预测 logreg_pred <- predict(logreg_model, newdata = test_data) svm_pred <- predict(svm_model, newdata = test_data) rf_pred <- predict(rf_model, newdata = test_data) knn_pred <- predict(knn_model, newdata = test_data) nb_pred <- predict(nb_model, newdata = test_data) nn_pred <- predict(nn_model, newdata = test_data) dt_pred <- predict(dt_model, newdata = test_data) # 对模型进行融合 final_pred <- (logreg_pred + svm_pred + rf_pred + knn_pred + nb_pred + nn_pred + dt_pred) / 7 # 计算融合模型的评估指标 confusionMatrix(as.factor(ifelse(final_pred > 0.5, 1, 0)), as.factor(test_data$Survived)) ``` 需要注意的是，R语言中的模型名称和Python语言中的不完全相同，因此需要根据R语言的函数库进行调整。此外，R语言中的模型训练和预测的函数与Python语言略有不同，需要根据具体情况进行调整。

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