Setting
rm(list=ls(all=TRUE))
setwd('C:/Users/sitdo/Documents/GitHub/IBD-EDA/paper1/')
Loading Data
library(dplyr)
载入程辑包:‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
data <- read.csv("./data_preprocessed/data.csv") %>% select(-1)
Installing Packages
library(e1071)
Method I: Splitting Data
set.seed(123)
splitting_ratio <- 0.7
indices <- 1:nrow(data)
shuffled_indices <- sample(indices)
train_size <- floor(splitting_ratio * length(indices))
train_indices <- shuffled_indices[1:train_size]
test_indices <- shuffled_indices[(train_size + 1):length(indices)]
train_data <- data[train_indices, ]
test_data <- data[test_indices, ]
Building Model
linear_svm_model <- svm(dod ~ ., data = train_data, kernel = "linear")
predictions <- predict(linear_svm_model, newdata = test_data)
Method II: Cross Validation
# Perform 10-fold cross-validation
num_folds <- 10
folds <- cut(seq(1, nrow(data)), breaks = num_folds, labels = FALSE)
# Create empty vectors to store the predictions and actual values
all_predictions <- vector()
all_actuals <- vector()
for (i in 1:num_folds) {
# Split the data into training and test sets for the current fold
train_data <- data[folds != i, ]
test_data <- data[folds == i, ]
train_X <- as.matrix(train_data[, -1])
train_y <- train_data[, 1]
test_X <- as.matrix(test_data[, -1])
test_y <- test_data[, 1]
# Train a Linear SVM model
linear_svm_model <- svm(dod ~ ., data = train_data, kernel = "linear")
# Make predictions using the trained models
predictions <- predict(linear_svm_model, newdata = test_data)
# Append the predictions and actual values to the vectors
all_predictions <- c(all_predictions, predictions)
all_actuals <- c(all_actuals, test_y)
}
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