[英]Split into training and testing set in R?
How can I write the following written code in python into R ?如何将以下用 python 编写的代码写入 R ?
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2, random_state=42)
Spliting into training and testing set 80/20 ratio.以 80/20 的比例拆分为训练集和测试集。
Probably the simpler way to do so可能是更简单的方法
#read in iris dataset
data(iris)
library(caret) #this package has the createDataPartition function
set.seed(123) #randomization`
#creating indices
trainIndex <- createDataPartition(iris$Species,p=0.75,list=FALSE)
#splitting data into training/testing data using the trainIndex object
IRIS_TRAIN <- iris[trainIndex,] #training data (75% of data)
IRIS_TEST <- iris[-trainIndex,] #testing data (25% of data)
You can do this using caret
's createDataPartition
function:您可以使用caret
的createDataPartition
函数执行此操作:
library(caret)
# Make example data
X = data.frame(matrix(rnorm(200), nrow = 100))
y = rnorm(100)
#Extract random sample of indices for test data
set.seed(42) #equivalent to python's random_state arg
test_inds = createDataPartition(y = 1:length(y), p = 0.2, list = F)
# Split data into test/train using indices
X_test = X[test_inds, ]; y_test = y[test_inds]
X_train = X[-test_inds, ]; y_train = y[-test_inds]
You could also create test_inds
'from scratch' using test_inds = sample(1:length(y), ceiling(length(y) * 0.2))
您还可以使用test_inds = sample(1:length(y), ceiling(length(y) * 0.2))
从头开始创建test_inds
Using base R you can do the following:使用基础 R,您可以执行以下操作:
set.seed(12345)
#getting training data set sizes of .20 (in this case 20 out of 100)
train.x<-sample(1:100, 20)
train.y<-sample(1:100, 20)
#simulating random data
x<-rnorm(100)
y<-rnorm(100)
#sub-setting the x data
training.x.data<-x[train]
testing.x.data<-x[-train]
#sub-setting the y data
training.y.data<-y[train]
testing.y.data<-y[-train]
Let's take the iris
dataset:让我们以iris
数据集为例:
# in case you want to use a seed
set.seed(5)
## 70% of the sample size
train_size <- floor(0.75 * nrow(iris))
in_rows <- sample(c(1:nrow(iris)), size = train_size, replace = FALSE)
train <- iris[in_rows, ]
test <- iris[-in_rows, ]
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