学习笔记，仅供参考，有错必纠，按需更新
PS：大量参考R的帮助文档，照搬英文不翻译
preProcess函数
所在包：caret
描述
Pre-processing transformation (centering, scaling etc.) can be estimated from the training data and applied to any data set with the same variables.
使用
preProcess(x, ...)
## Default S3 method:
preProcess(x, method = c("center", "scale"),
thresh = 0.95, pcaComp = NULL, na.remove = TRUE, k = 5,
knnSummary = mean, outcome = NULL, fudge = 0.2, numUnique = 3,
verbose = FALSE, freqCut = 95/5, uniqueCut = 10, cutoff = 0.9,
rangeBounds = c(0, 1), ...)
## S3 method for class 'preProcess'
predict(object, newdata, ...)
参数
参数 | 解释 |
x | a matrix or data frame. Non-numeric predictors are allowed but will be ignored. |
method | a character vector specifying the type of processing. Possible values are “BoxCox”, “YeoJohnson”, “expoTrans”, “center”, “scale”, “range”, “knnImpute”, “bagImpute”, “medianImpute”, “pca”, “ica”, “spatialSign”, “corr”, “zv”, “nzv”, and “conditionalX” (see Details below) |
举个例子
dfTest3 <- iris[, -length(iris)]
head(dfTest3, 3)
centerDf <- preProcess(dfTest3) #默认中心化标准化
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)
head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "center") #中心化
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)
head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "BoxCox") #BoxCox变换
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)
head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "range") #取值在0-1之间
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)输出：
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 -0.8976739 1.0156020 -1.335752 -1.311052
2 -1.1392005 -0.1315388 -1.335752 -1.311052
3 -1.3807271 0.3273175 -1.392399 -1.311052
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 -0.7433333 0.44266667 -2.358 -0.9993333
2 -0.9433333 -0.05733333 -2.358 -0.9993333
3 -1.1433333 0.14266667 -2.458 -0.9993333
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 1.629241 1.520660 1.4 -1.032115
2 1.589235 1.301297 1.4 -1.032115
3 1.547563 1.391905 1.3 -1.032115
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 0.2222222 0.6250000 0.06779661 0.04166667
2 0.1666667 0.4166667 0.06779661 0.04166667
3 0.1111111 0.5000000 0.05084746 0.04166667
