22.2 Internal and External Performance Estimates. 22 Feature Selection using Simulated Annealing. 21.2 Internal and External Performance Estimates. 21 Feature Selection using Genetic Algorithms. 20.3 Recursive Feature Elimination via caret. 20.2 Resampling and External Validation. 19 Feature Selection using Univariate Filters. 18.1 Models with Built-In Feature Selection. 16.6 Neural Networks with a Principal Component Step. 16.2 Partial Least Squares Discriminant Analysis. 16.1 Yet Another k-Nearest Neighbor Function. 13.9 Illustrative Example 6: Offsets in Generalized Linear Models. 13.8 Illustrative Example 5: Optimizing probability thresholds for class imbalances. 13.7 Illustrative Example 4: PLS Feature Extraction Pre-Processing. 13.6 Illustrative Example 3: Nonstandard Formulas. 13.5 Illustrative Example 2: Something More Complicated - LogitBoost. 13.2 Illustrative Example 1: SVMs with Laplacian Kernels. 12.1.2 Using additional data to measure performance. 12.1.1 More versatile tools for preprocessing data. 11.4 Using Custom Subsampling Techniques. 7.0.27 Multivariate Adaptive Regression Splines. 5.9 Fitting Models Without Parameter Tuning. 5.8 Exploring and Comparing Resampling Distributions. 5.7 Extracting Predictions and Class Probabilities. 
5.1 Model Training and Parameter Tuning. 4.4 Simple Splitting with Important Groups. 4.1 Simple Splitting Based on the Outcome. 3.2 Zero- and Near Zero-Variance Predictors. Head(training) # Ozone Solar.R Wind Temp Month Day ind <- createDataPartition(y=DataImputeBag$Ozone, p=0.6, list=FALSE) Some variable has large range, for example: rainfall (0-1000mm), temperature (-10 to 40oC), humidity (0-100%), etc. Head(DataImputeBag2) # Ozone Solar.R Wind Temp Month Day PreImputeBag2 <- preProcess(airquality, method="knnImpute") # normalizationĭataImputeBag2 <- predict(PreImputeBag2, airquality) Head(DataImputeBag) # Ozone Solar.R Wind Temp Month Day PreImputeBag <- preProcess(airquality, method="bagImpute")ĭataImputeBag <- predict(PreImputeBag, airquality) 
While, in theory, this is a more powerful method of imputing, the computational costs are much higher than the nearest neighbor technique.
When a new sample has a missing predictor value, the bagged model is used to predict the value.
For each predictor in the data, a bagged tree is created using all of the other predictors in the training set. New_airquality2 <-replace(airquality, TRUE, lapply(airquality, NA2mean))ĭim(new_airquality2) # 153 6 head(new_airquality2) # Ozone Solar.R Wind Temp Month DayĪlternatively, bagged trees can also be used to impute. NA2mean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE)) # 6 28 NA 14.9 66 5 6 new_airquality1 <- na.omit(airquality)ĭim(new_airquality1) # 111 6 head(new_airquality1) # Ozone Solar.R Wind Temp Month Day library(base)ĭim(airquality) # 153 6 head(airquality) # Ozone Solar.R Wind Temp Month Day Load dataset airquality( New York Air Quality Measurements): Daily air quality measurements in New York, May to September 1973. # Levels: setosa versicolor virginica table(iris$Species) # # virginica virginica virginica virginica virginica virginica # versicolor versicolor versicolor versicolor virginica virginica # versicolor versicolor versicolor versicolor versicolor versicolor # setosa setosa versicolor versicolor versicolor versicolor # setosa setosa setosa setosa setosa setosa # "Species" factor(iris$Species) # setosa setosa setosa setosa setosa setosa rm(list=ls())ĭim(iris) # 150 5 names(iris) # "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" The species are Iris setosa, versicolor, and virginica. Load dataset ‘iris’( Edgar Anderson’s Iris Data): This famous (Fisher’s or Anderson’s) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris.
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