如何使用r进行多分类并行运行knn算法

1 回答

• 1

  you can use the following by modifying it accordig to KNN .. If need i will provided you with exact code .. the following code is for svc
  
  
  
  
  
  pkgs <- c('foreach', 'doParallel')
  
  lapply(pkgs, require, character.only = T)
  
  registerDoParallel(cores = 4)
  
  ### PREPARE FOR THE DATA ###
  
  df1 <- read.csv(...... your dataset path........)
  
  ## do normalization if needed ##
  
  
  ### SPLIT DATA INTO K FOLDS ###
  set.seed(2016)
  
  df1$fold <- caret::createFolds(1:nrow(df1), k = 10, list = FALSE)
  
  
  ### PARAMETER LIST ###
  cost <- 10^(-1:4)
  
  gamma <- 2^(-4:-1)
  
  parms <- expand.grid(cost = cost, gamma = gamma)
  
  ### LOOP THROUGH PARAMETER VALUES ###
  result <- foreach(i = 1:nrow(parms), .combine = rbind) %do% {
  
    c <- parms[i, ]$cost
  
    g <- parms[i, ]$gamma
  
    ### K-FOLD VALIDATION ###
  
    out <- foreach(j = 1:max(df1$fold), .combine = rbind, .inorder = FALSE) %dopar% {
  
  deve <- df1[df1$fold != j, ]
  
      test <- df1[df1$fold == j, ]
  
     mdl <- e1071::svm(Classification-type-column ~ ., data = deve, type = "C-classification", kernel = "radial", cost = c, gamma = g, probability = TRUE)
  
      pred <- predict(mdl, test, decision.values = TRUE, probability = TRUE)
      data.frame(y = test$DEFAULT, prob = attributes(pred)$probabilities[, 2])
  
    }
    ### CALCULATE SVM PERFORMANCE ###
  
    roc <- pROC::roc(as.factor(out$y), out$prob) 
  
    data.frame(parms[i, ], roc = roc$auc[1])
  
  }
  

  回复于 2024-05-03T14:59:17+08:00