學習筆記，僅供參考，有錯必糾，按需更新

PS：大量參考R的幫助文檔，照搬英文不翻譯

---

preProcess函數(shù)

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, ...)

參數(shù)

參數(shù)解釋

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)

舉個例子

dfTest3 <- iris[, -length(iris)]head(dfTest3, 3) centerDf <- preProcess(dfTest3) #默認中心化標準化 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變換 pre_Df <- predict(centerDf, dfTest3) head(pre_Df, 3)head(dfTest3, 3) centerDf <- preProcess(dfTest3, method = "range") #取值在0-1之間 pre_Df <- predict(centerDf, dfTest3) head(pre_Df, 3)

輸出：

> 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

總結(jié)

以上是生活随笔為你收集整理的R语言观察日志(part2)--preProcess函数的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。