HW4
Gillian McGovern
2025-02-26
HW 4
Do problems 3.1 and 3.2 in the Kuhn and Johnson book Applied Predictive Modeling. Please submit your Rpubs link along with your .pdf for your run code.
library(mlbench)
library(e1071)
library(corrplot)## corrplot 0.95 loadedlibrary(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(tidyr)
library(ggplot2)
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## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
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library(ellipse)##
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## pairs3.1. The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. The data can be accessed via:
- Using visualizations, explore the predictor variables to understand their distributions as well as the relationships between predictors.
RI distribution:
library(mlbench)
data(Glass)
# RI
histogram(Glass$RI,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$RI),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(RI ~ Na,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Na",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Mg,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Mg",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Al,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Al",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Si,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Si",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ K,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "K",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Ca",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(RI ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "RI",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
Na:
# Na
histogram(~Glass$Na,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Na),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Na ~ Mg,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Mg",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ Al,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Al",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ Si,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Si",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ K,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "K",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Ca",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Na ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Na",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Mg
histogram(~Glass$Mg,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Mg),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Mg ~ Al,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "Al",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Mg ~ Si,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "Si",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Mg ~ K,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "K",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Mg ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "Na",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Mg ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Mg ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Mg",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Al
histogram(~Glass$Al,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Al),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Al ~ Si,
data = Glass,
groups = Glass$Type,
xlab = "Al",
ylab = "Si",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Al ~ K,
data = Glass,
groups = Glass$Type,
xlab = "Al",
ylab = "K",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Al ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "Al",
ylab = "Ca",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Al ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "Al",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Al ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Al",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Si
histogram(~Glass$Si,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Si),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Si ~ K,
data = Glass,
groups = Glass$Type,
xlab = "Si",
ylab = "K",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Si ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "Si",
ylab = "Ca",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Si ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "Si",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Si ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Si",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# K
histogram(~Glass$K,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$K),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(K ~ Ca,
data = Glass,
groups = Glass$Type,
xlab = "K",
ylab = "Ca",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(K ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "K",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(K ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "K",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Ca
histogram(~Glass$Ca,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Ca),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Ca ~ Ba,
data = Glass,
groups = Glass$Type,
xlab = "Ca",
ylab = "Ba",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
xyplot(Ca ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Ca",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Ba
histogram(~Glass$Ba,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Ba),
xlab = "Log Units",
ylab = " ",
type = "count")
xyplot(Ba ~ Fe,
data = Glass,
groups = Glass$Type,
xlab = "Ba",
ylab = "Fe",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
# Fe
histogram(~Glass$Fe,
xlab = "Natural Units",
type = "count")
histogram(~log(Glass$Fe),
xlab = "Log Units",
ylab = " ",
type = "count")
df <- Glass[, -10]
correlations <- cor(df, use="pairwise.complete.obs")
dim(correlations)## [1] 9 9corrplot(correlations, order = "hclust")
highCorr <- findCorrelation(correlations, cutoff = .75)
length(highCorr)## [1] 1head(highCorr)## [1] 7- Do there appear to be any outliers in the data? Are any predictors skewed?
Looking at the histograms, predictors containing outliers are:
- K (has some outliers with a value of 6)
- Mg (with a value of 0 could have outliers as it doesn’t entirely match with trend of the graph)
- Ba (with potential outliers with a value 3)
Looking at the scatter plots, the predictors containing outliers are:
- K vs RI — outlier for type 5 away from the cluster
- Ba vs RI — outlier for type 2 and 5 away from the cluster
- Si vs Na - outliers for a few, namely type 6
- K vs Na — outlier for type 5
- Ba vs Na — outlier for tyoe 5 and tyoe 2
- K vs Mg — outlier for type 5
- Ba vs Al - outlier for 2 and 5
- Fe and Al - outlier for type 5
- K vs Si - outlier for 5 and 2
- Ca vs K - outlieter for type 5
- Ba vs K - outlier for type 5
- Fe vs K - outlier for tyope 5
- Ba vs Ca - outlier for 2 and 5
- Fe va Ba - outlier for 5
Looking at the histograms, predictors that are skewed are:
- RI - looks right skewed
- Na - looks slightly right skewed
- Mg - looks left skewed
- Al - looks right skewed
- Si - looks slightly left skewed
- K - looks right skewed
- Ca - looks right skewed
- Ba - looks right skewed
- Fe - looks right skewed
Let’s take a look at the actual skew values:
skewValues <- apply(df, 2, skewness)
skewValues## RI Na Mg Al Si K Ca
## 1.6027151 0.4478343 -1.1364523 0.8946104 -0.7202392 6.4600889 2.0184463
## Ba Fe
## 3.3686800 1.7298107These seem to match up with the histograms where K and Ba are heavily right skewed. They both have a lot of outliers so this makes sense.
- Are there any relevant transformations of one or more predictors that might improve the classification model?
Let’s try for skewness by caret’s preProcess function.
## Use caret's preProcess function to transform for skewness
segPP <- preProcess(Glass, method = "BoxCox")
## Apply the transformations
glassTrans <- predict(segPP, Glass)
## Results for a single predictor
segPP$bc## $RI
## Box-Cox Transformation
##
## 214 data points used to estimate Lambda
##
## Input data summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.511 1.517 1.518 1.518 1.519 1.534
##
## Largest/Smallest: 1.02
## Sample Skewness: 1.6
##
## Estimated Lambda: -2
##
##
## $Na
## Box-Cox Transformation
##
## 214 data points used to estimate Lambda
##
## Input data summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.73 12.91 13.30 13.41 13.82 17.38
##
## Largest/Smallest: 1.62
## Sample Skewness: 0.448
##
## Estimated Lambda: -0.1
## With fudge factor, Lambda = 0 will be used for transformations
##
##
## $Al
## Box-Cox Transformation
##
## 214 data points used to estimate Lambda
##
## Input data summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.290 1.190 1.360 1.445 1.630 3.500
##
## Largest/Smallest: 12.1
## Sample Skewness: 0.895
##
## Estimated Lambda: 0.5
##
##
## $Si
## Box-Cox Transformation
##
## 214 data points used to estimate Lambda
##
## Input data summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 69.81 72.28 72.79 72.65 73.09 75.41
##
## Largest/Smallest: 1.08
## Sample Skewness: -0.72
##
## Estimated Lambda: 2
##
##
## $Ca
## Box-Cox Transformation
##
## 214 data points used to estimate Lambda
##
## Input data summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.430 8.240 8.600 8.957 9.172 16.190
##
## Largest/Smallest: 2.98
## Sample Skewness: 2.02
##
## Estimated Lambda: -1.1histogram(Glass$Ca,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(~glassTrans$Ca,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(Glass$Si,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(~glassTrans$Si,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(Glass$Al,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(~glassTrans$Al,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(Glass$Na,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(~glassTrans$Na,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(Glass$RI,
xlab = "Transformed Data",
ylab = " ",
type = "count")
histogram(~glassTrans$RI,
xlab = "Transformed Data",
ylab = " ",
type = "count")
## Apply PCA to the entire set of predictors.
glassTest <- Glass[,-10]
isZV <- apply(glassTest, 2, function(x) length(unique(x)) == 1)
glassX <- glassTest[, !isZV]
glassPP <- preProcess(glassX, c("BoxCox", "center", "scale"))
glassTrans <- predict(glassPP, glassX)
glassPCA <- prcomp(glassTrans, center = TRUE, scale. = TRUE)
## Plot a scatterplot matrix of the first three components
transparentTheme(pchSize = .8, trans = .3)
panelRange <- extendrange(glassPCA$x[, 1:3])
splom(as.data.frame(glassPCA$x[, 1:3]),
groups = Glass$Type,
type = c("p", "g"),
as.table = TRUE,
auto.key = list(columns = 2),
prepanel.limits = function(x) panelRange)
Since we also have some high correlation, we could also look into removing those variables.
3.2. The soybean data can also be found at the UC Irvine Machine Learning Repository. Data were collected to predict disease in 683 soybeans. The 35 predictors are mostly categorical and include information on the environmental conditions (e.g., temperature, precipitation) and plant conditions (e.g., left spots, mold growth). The outcome labels consist of 19 distinct classes.
The data can be loaded via:
library(mlbench) data(Soybean) ## See ?Soybean for details
- Investigate the frequency distributions for the categorical predictors. Are any of the distributions degenerate in the ways discussed earlier in this chapter?
# Class
data(Soybean)
histogram(~Soybean$Class,
xlab = "Natural Units",
type = "count")
# date
histogram(~Soybean$date,
xlab = "Natural Units",
type = "count")
# plant.stand
histogram(~Soybean$ plant.stand,
xlab = "Natural Units",
type = "count")
# precip
histogram(~Soybean$precip,
xlab = "Natural Units",
type = "count")
# temp
histogram(~Soybean$temp,
xlab = "Natural Units",
type = "count")
# hail
histogram(~Soybean$hail,
xlab = "Natural Units",
type = "count")
# crop.hist
histogram(~Soybean$crop.hist,
xlab = "Natural Units",
type = "count")
# area.dam
histogram(~Soybean$area.dam,
xlab = "Natural Units",
type = "count")
# sever
histogram(~Soybean$sever,
xlab = "Natural Units",
type = "count")
# seed.tmt
histogram(~Soybean$seed.tmt,
xlab = "Natural Units",
type = "count")
# germ
histogram(~Soybean$germ,
xlab = "Natural Units",
type = "count")
# plant.growth
histogram(~Soybean$plant.growth,
xlab = "Natural Units",
type = "count")
# leaves
histogram(~Soybean$leaves,
xlab = "Natural Units",
type = "count")
# leaf.halo
histogram(~Soybean$leaf.halo,
xlab = "Natural Units",
type = "count")
# leaf.marg
histogram(~Soybean$leaf.marg,
xlab = "Natural Units",
type = "count")
# leaf.size
histogram(~Soybean$leaf.size,
xlab = "Natural Units",
type = "count")
# leaf.shread
histogram(~Soybean$leaf.shread,
xlab = "Natural Units",
type = "count")
# leaf.malf
histogram(~Soybean$leaf.malf,
xlab = "Natural Units",
type = "count")
# leaf.malf
histogram(~Soybean$leaf.malf,
xlab = "Natural Units",
type = "count")
# stem
histogram(~Soybean$stem,
xlab = "Natural Units",
type = "count")
# lodging
histogram(~Soybean$lodging,
xlab = "Natural Units",
type = "count")
# stem.cankers
histogram(~Soybean$stem.cankers,
xlab = "Natural Units",
type = "count")
# canker.lesion
histogram(~Soybean$canker.lesion,
xlab = "Natural Units",
type = "count")
# fruiting.bodies
histogram(~Soybean$fruiting.bodies,
xlab = "Natural Units",
type = "count")
# ext.decay
histogram(~Soybean$ext.decay,
xlab = "Natural Units",
type = "count")
# mycelium
histogram(~Soybean$mycelium,
xlab = "Natural Units",
type = "count")
# int.discolor
histogram(~Soybean$int.discolor,
xlab = "Natural Units",
type = "count")
# sclerotia
histogram(~Soybean$sclerotia,
xlab = "Natural Units",
type = "count")
# fruit.pods
histogram(~Soybean$fruit.pods,
xlab = "Natural Units",
type = "count")
# fruit.spots
histogram(~Soybean$fruit.spots,
xlab = "Natural Units",
type = "count")
# seed
histogram(~Soybean$seed,
xlab = "Natural Units",
type = "count")
# mold.growth
histogram(~Soybean$mold.growth,
xlab = "Natural Units",
type = "count")
# seed.discolor
histogram(~Soybean$seed.discolor,
xlab = "Natural Units",
type = "count")
# seed.size
histogram(~Soybean$seed.size,
xlab = "Natural Units",
type = "count")
# shriveling
histogram(~Soybean$shriveling,
xlab = "Natural Units",
type = "count")
# roots
histogram(~Soybean$roots,
xlab = "Natural Units",
type = "count")
Most of these graphs are right skewed, but:
- leaf size
- leaf halo
- leaves
- plant growth
- germ
- sever
- crop hist
- temp
- precip
xyplot(seed.discolor ~ seed.size,
data = Soybean,
groups = Soybean$Class,
xlab = "shriveling",
ylab = "seed.size",
auto.key = list(columns = 2),
type = c("p", "g"),
main = "Original Data",
aspect = 1)
Based on the values for predictors, there aren’t crazy outliers since most have a binary value.
- Roughly 18 % of the data are missing. Are there particular predictors that are more likely to be missing? Is the pattern of missing data related to the classes?
Let’s count about how many missing values each predictor has:
sapply(Soybean, function(x) sum(is.na(x)))## Class date plant.stand precip temp
## 0 1 36 38 30
## hail crop.hist area.dam sever seed.tmt
## 121 16 1 121 121
## germ plant.growth leaves leaf.halo leaf.marg
## 112 16 0 84 84
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 84 100 84 108 16
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 121 38 38 106 38
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 38 38 38 84 106
## seed mold.growth seed.discolor seed.size shriveling
## 92 92 106 92 106
## roots
## 31Based on these counts, the predictors that are most likely to have missing values are:
- lodging
- sever
- hail
Now let’s do the same for each class:
for (name in unique(Soybean$Class)) {
print(name)
soybeanFiltered <- Soybean |>
filter(Class == name)
print(sapply(soybeanFiltered, function(x) sum(is.na(x))))
}## [1] "diaporthe-stem-canker"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "charcoal-rot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "rhizoctonia-root-rot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "phytophthora-rot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 68 0 0 68 68
## germ plant.growth leaves leaf.halo leaf.marg
## 68 0 0 55 55
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 55 55 55 55 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 68 0 0 68 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 68 68
## seed mold.growth seed.discolor seed.size shriveling
## 68 68 68 68 68
## roots
## 0
## [1] "brown-stem-rot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "powdery-mildew"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "downy-mildew"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "brown-spot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "bacterial-blight"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "bacterial-pustule"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "purple-seed-stain"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "anthracnose"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "phyllosticta-leaf-spot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "alternarialeaf-spot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "frog-eye-leaf-spot"
## Class date plant.stand precip temp
## 0 0 0 0 0
## hail crop.hist area.dam sever seed.tmt
## 0 0 0 0 0
## germ plant.growth leaves leaf.halo leaf.marg
## 0 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 0 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 0 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 0
## [1] "diaporthe-pod-&-stem-blight"
## Class date plant.stand precip temp
## 0 0 6 0 0
## hail crop.hist area.dam sever seed.tmt
## 15 0 0 15 15
## germ plant.growth leaves leaf.halo leaf.marg
## 6 0 0 15 15
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 15 15 15 15 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 15 0 0 0 0
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 0 0 0 0 0
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 0 0 0
## roots
## 15
## [1] "cyst-nematode"
## Class date plant.stand precip temp
## 0 0 14 14 14
## hail crop.hist area.dam sever seed.tmt
## 14 0 0 14 14
## germ plant.growth leaves leaf.halo leaf.marg
## 14 0 0 14 14
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 14 14 14 14 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 14 14 14 14 14
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 14 14 14 0 14
## seed mold.growth seed.discolor seed.size shriveling
## 0 0 14 0 14
## roots
## 0
## [1] "2-4-d-injury"
## Class date plant.stand precip temp
## 0 1 16 16 16
## hail crop.hist area.dam sever seed.tmt
## 16 16 1 16 16
## germ plant.growth leaves leaf.halo leaf.marg
## 16 16 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 16 0 16 16
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 16 16 16 16 16
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 16 16 16 16 16
## seed mold.growth seed.discolor seed.size shriveling
## 16 16 16 16 16
## roots
## 16
## [1] "herbicide-injury"
## Class date plant.stand precip temp
## 0 0 0 8 0
## hail crop.hist area.dam sever seed.tmt
## 8 0 0 8 8
## germ plant.growth leaves leaf.halo leaf.marg
## 8 0 0 0 0
## leaf.size leaf.shread leaf.malf leaf.mild stem
## 0 0 0 8 0
## lodging stem.cankers canker.lesion fruiting.bodies ext.decay
## 8 8 8 8 8
## mycelium int.discolor sclerotia fruit.pods fruit.spots
## 8 8 8 0 8
## seed mold.growth seed.discolor seed.size shriveling
## 8 8 8 8 8
## roots
## 0These are the classes that have missing data:
- phytophthora-rot
- diaporthe-pod-&-stem-blight
- cyst-nematode
- 2-4-d-injury
- herbicide-injury
- Develop a strategy for handling missing data, either by eliminating predictors or imputation.
I tried to install the impute package as mentioned in
the book, but the one they called out did not come up as an option when
searching in R Studio. So I used the VIM package
instead:
soybeanTest <- Soybean |>
kNN(variable = names(Soybean[, -1]),
k = 5,
numFun = weighted.mean,
weightDist = TRUE)
soybeanTest## Class date plant.stand precip temp hail crop.hist
## 1 diaporthe-stem-canker 6 0 2 1 0 1
## 2 diaporthe-stem-canker 4 0 2 1 0 2
## 3 diaporthe-stem-canker 3 0 2 1 0 1
## 4 diaporthe-stem-canker 3 0 2 1 0 1
## 5 diaporthe-stem-canker 6 0 2 1 0 2
## 6 diaporthe-stem-canker 5 0 2 1 0 3
## 7 diaporthe-stem-canker 5 0 2 1 0 2
## 8 diaporthe-stem-canker 4 0 2 1 1 1
## 9 diaporthe-stem-canker 6 0 2 1 0 3
## 10 diaporthe-stem-canker 4 0 2 1 0 2
## 11 charcoal-rot 6 0 0 2 0 1
## 12 charcoal-rot 4 0 0 1 1 1
## 13 charcoal-rot 3 0 0 1 0 1
## 14 charcoal-rot 6 0 0 1 1 3
## 15 charcoal-rot 6 0 0 2 0 1
## 16 charcoal-rot 5 0 0 2 1 3
## 17 charcoal-rot 6 0 0 2 1 0
## 18 charcoal-rot 4 0 0 1 0 2
## 19 charcoal-rot 3 0 0 2 0 2
## 20 charcoal-rot 5 0 0 2 1 2
## 21 rhizoctonia-root-rot 1 1 2 0 0 2
## 22 rhizoctonia-root-rot 1 1 2 0 0 1
## 23 rhizoctonia-root-rot 3 0 2 0 1 3
## 24 rhizoctonia-root-rot 0 1 2 0 0 0
## 25 rhizoctonia-root-rot 0 1 2 0 0 1
## 26 rhizoctonia-root-rot 1 1 2 0 0 3
## 27 rhizoctonia-root-rot 1 1 2 0 0 0
## 28 rhizoctonia-root-rot 2 1 2 0 0 2
## 29 rhizoctonia-root-rot 1 1 2 0 0 1
## 30 rhizoctonia-root-rot 2 1 2 0 0 1
## 31 phytophthora-rot 0 1 2 1 1 1
## 32 phytophthora-rot 1 1 2 1 0 3
## 33 phytophthora-rot 2 1 2 2 0 2
## 34 phytophthora-rot 1 1 2 0 0 2
## 35 phytophthora-rot 2 1 2 2 0 2
## 36 phytophthora-rot 3 1 2 1 0 2
## 37 phytophthora-rot 0 1 1 1 0 1
## 38 phytophthora-rot 3 1 2 0 0 2
## 39 phytophthora-rot 2 1 1 1 0 0
## 40 phytophthora-rot 2 1 2 0 0 1
## 41 phytophthora-rot 2 1 2 1 0 1
## 42 phytophthora-rot 1 1 2 1 0 1
## 43 phytophthora-rot 0 1 2 1 0 3
## 44 phytophthora-rot 0 1 1 1 1 2
## 45 phytophthora-rot 3 1 2 0 0 1
## 46 phytophthora-rot 2 1 2 2 0 3
## 47 phytophthora-rot 0 1 2 1 0 2
## 48 phytophthora-rot 2 1 1 2 0 2
## 49 phytophthora-rot 2 1 2 1 1 1
## 50 phytophthora-rot 0 1 2 1 0 3
## 51 phytophthora-rot 1 1 2 1 0 0
## 52 phytophthora-rot 1 1 2 1 0 0
## 53 phytophthora-rot 3 1 2 1 0 1
## 54 phytophthora-rot 2 1 2 1 0 1
## 55 phytophthora-rot 3 1 2 2 0 2
## 56 phytophthora-rot 1 1 2 1 1 3
## 57 phytophthora-rot 3 1 1 1 0 3
## 58 phytophthora-rot 2 1 2 2 0 1
## 59 phytophthora-rot 3 1 1 2 0 2
## 60 phytophthora-rot 1 1 2 2 0 1
## 61 phytophthora-rot 2 1 2 2 0 3
## 62 phytophthora-rot 3 1 1 1 0 0
## 63 phytophthora-rot 2 1 2 0 0 1
## 64 phytophthora-rot 3 1 1 1 0 1
## 65 phytophthora-rot 2 1 2 2 0 1
## 66 phytophthora-rot 1 1 2 0 0 0
## 67 phytophthora-rot 3 1 2 1 0 2
## 68 phytophthora-rot 3 1 2 1 0 3
## 69 phytophthora-rot 3 1 1 0 0 2
## 70 phytophthora-rot 3 1 2 2 0 2
## 71 brown-stem-rot 4 0 0 1 0 1
## 72 brown-stem-rot 4 0 0 1 0 1
## 73 brown-stem-rot 3 1 0 0 0 3
## 74 brown-stem-rot 5 0 0 2 0 1
## 75 brown-stem-rot 5 0 0 2 0 2
## 76 brown-stem-rot 4 0 0 1 0 3
## 77 brown-stem-rot 5 0 0 1 1 3
## 78 brown-stem-rot 6 0 1 1 1 2
## 79 brown-stem-rot 5 1 0 0 0 3
## 80 brown-stem-rot 5 1 0 1 0 1
## 81 brown-stem-rot 4 0 1 0 1 2
## 82 brown-stem-rot 4 1 0 0 0 3
## 83 brown-stem-rot 4 0 0 1 0 2
## 84 brown-stem-rot 3 1 0 0 0 2
## 85 brown-stem-rot 5 0 0 1 0 3
## 86 brown-stem-rot 4 0 0 1 0 3
## 87 brown-stem-rot 4 0 0 1 0 3
## 88 brown-stem-rot 4 0 0 1 0 1
## 89 brown-stem-rot 4 0 0 1 0 1
## 90 brown-stem-rot 3 0 0 1 0 3
## 91 powdery-mildew 5 0 0 1 1 3
## 92 powdery-mildew 6 0 1 0 1 0
## 93 powdery-mildew 1 1 0 1 0 3
## 94 powdery-mildew 6 1 1 0 0 2
## 95 powdery-mildew 4 1 1 0 0 2
## 96 powdery-mildew 6 0 0 1 1 1
## 97 powdery-mildew 2 1 1 0 0 2
## 98 powdery-mildew 6 1 0 1 0 1
## 99 powdery-mildew 5 1 0 1 0 1
## 100 powdery-mildew 1 1 0 1 0 1
## 101 downy-mildew 6 0 2 0 1 2
## 102 downy-mildew 2 0 2 1 1 1
## 103 downy-mildew 1 0 2 1 1 3
## 104 downy-mildew 4 1 2 2 0 2
## 105 downy-mildew 1 0 2 0 1 0
## 106 downy-mildew 2 1 2 0 0 3
## 107 downy-mildew 2 1 2 1 0 2
## 108 downy-mildew 4 1 2 2 0 2
## 109 downy-mildew 4 1 2 0 0 1
## 110 downy-mildew 5 1 2 1 0 3
## 111 brown-spot 1 1 2 2 1 3
## 112 brown-spot 2 0 2 1 0 2
## 113 brown-spot 2 0 2 1 0 2
## 114 brown-spot 2 0 2 1 0 1
## 115 brown-spot 1 1 2 2 1 3
## 116 brown-spot 1 1 2 1 0 2
## 117 brown-spot 0 1 2 2 1 3
## 118 brown-spot 2 0 2 1 0 2
## 119 brown-spot 1 0 2 1 0 2
## 120 brown-spot 2 1 2 1 0 3
## 121 brown-spot 5 0 2 1 0 2
## 122 brown-spot 1 1 2 1 0 3
## 123 brown-spot 1 0 2 1 0 3
## 124 brown-spot 4 0 2 1 0 1
## 125 brown-spot 1 0 2 1 0 2
## 126 brown-spot 4 1 2 1 0 3
## 127 brown-spot 2 0 2 1 0 3
## 128 brown-spot 0 1 1 1 1 2
## 129 brown-spot 1 1 1 1 1 2
## 130 brown-spot 1 1 2 1 0 1
## 131 brown-spot 1 0 2 1 0 1
## 132 brown-spot 2 0 2 1 0 3
## 133 brown-spot 3 0 2 1 0 2
## 134 brown-spot 2 1 2 2 1 3
## 135 brown-spot 1 0 2 1 0 2
## 136 brown-spot 1 1 2 1 0 2
## 137 brown-spot 5 0 2 1 0 1
## 138 brown-spot 4 1 1 1 1 2
## 139 brown-spot 3 1 2 1 0 1
## 140 brown-spot 1 0 2 1 0 3
## 141 brown-spot 4 0 2 1 0 2
## 142 brown-spot 2 1 2 1 0 2
## 143 brown-spot 2 1 1 1 1 0
## 144 brown-spot 3 1 2 1 0 3
## 145 brown-spot 3 0 2 1 0 3
## 146 brown-spot 2 0 2 1 0 2
## 147 brown-spot 3 0 2 1 0 3
## 148 brown-spot 3 1 2 1 0 3
## 149 brown-spot 2 1 2 1 0 3
## 150 brown-spot 5 1 2 1 0 3
## 151 bacterial-blight 5 0 2 1 1 3
## 152 bacterial-blight 4 0 2 2 1 2
## 153 bacterial-blight 2 0 1 1 0 0
## 154 bacterial-blight 3 0 1 1 0 1
## 155 bacterial-blight 3 0 1 1 0 3
## 156 bacterial-blight 3 0 2 1 1 2
## 157 bacterial-blight 3 0 1 1 0 1
## 158 bacterial-blight 4 0 2 1 1 0
## 159 bacterial-blight 2 0 1 1 0 3
## 160 bacterial-blight 4 1 2 2 1 2
## 161 bacterial-pustule 2 1 1 2 0 2
## 162 bacterial-pustule 3 0 2 0 1 2
## 163 bacterial-pustule 2 0 1 0 0 0
## 164 bacterial-pustule 4 1 2 1 0 3
## 165 bacterial-pustule 3 0 2 1 1 1
## 166 bacterial-pustule 3 1 1 0 0 2
## 167 bacterial-pustule 3 0 1 1 1 2
## 168 bacterial-pustule 3 1 2 1 0 0
## 169 bacterial-pustule 4 0 1 1 1 1
## 170 bacterial-pustule 5 1 1 1 0 2
## 171 purple-seed-stain 6 0 2 0 1 2
## 172 purple-seed-stain 6 0 2 0 0 2
## 173 purple-seed-stain 4 0 2 1 1 1
## 174 purple-seed-stain 4 0 2 1 1 0
## 175 purple-seed-stain 4 0 2 0 0 0
## 176 purple-seed-stain 6 0 2 2 0 2
## 177 purple-seed-stain 3 0 2 0 1 0
## 178 purple-seed-stain 3 0 2 1 1 3
## 179 purple-seed-stain 5 0 2 1 0 1
## 180 purple-seed-stain 4 0 2 1 0 0
## 181 anthracnose 5 1 2 1 0 3
## 182 anthracnose 5 1 2 2 1 2
## 183 anthracnose 6 0 2 1 0 1
## 184 anthracnose 2 1 2 2 1 0
## 185 anthracnose 3 0 2 1 0 3
## 186 anthracnose 4 1 2 2 1 2
## 187 anthracnose 6 0 2 1 0 2
## 188 anthracnose 1 0 2 1 0 1
## 189 anthracnose 6 1 2 1 0 2
## 190 anthracnose 5 0 2 1 0 1
## 191 anthracnose 5 1 2 2 1 3
## 192 anthracnose 0 0 2 1 0 3
## 193 anthracnose 6 0 2 1 0 2
## 194 anthracnose 5 1 2 1 0 1
## 195 anthracnose 5 0 2 1 0 2
## 196 anthracnose 6 1 2 2 1 0
## 197 anthracnose 5 0 2 1 0 1
## 198 anthracnose 6 1 2 2 1 3
## 199 anthracnose 5 1 2 1 0 3
## 200 anthracnose 5 1 2 1 0 2
## 201 phyllosticta-leaf-spot 3 1 1 1 0 0
## 202 phyllosticta-leaf-spot 3 0 0 1 1 0
## 203 phyllosticta-leaf-spot 3 1 1 1 0 0
## 204 phyllosticta-leaf-spot 3 0 0 1 1 2
## 205 phyllosticta-leaf-spot 3 1 1 2 0 3
## 206 phyllosticta-leaf-spot 2 0 0 1 1 0
## 207 phyllosticta-leaf-spot 1 0 0 2 1 3
## 208 phyllosticta-leaf-spot 2 1 1 1 0 2
## 209 phyllosticta-leaf-spot 2 0 0 2 1 3
## 210 phyllosticta-leaf-spot 2 1 1 2 0 3
## 211 alternarialeaf-spot 4 1 2 1 0 1
## 212 alternarialeaf-spot 4 0 1 1 0 3
## 213 alternarialeaf-spot 3 0 2 1 0 0
## 214 alternarialeaf-spot 6 0 2 2 0 3
## 215 alternarialeaf-spot 6 0 1 1 1 2
## 216 alternarialeaf-spot 5 0 2 2 0 3
## 217 alternarialeaf-spot 6 0 1 1 0 3
## 218 alternarialeaf-spot 5 1 2 2 0 3
## 219 alternarialeaf-spot 6 0 2 2 0 3
## 220 alternarialeaf-spot 6 0 2 2 0 3
## 221 alternarialeaf-spot 5 0 2 2 0 2
## 222 alternarialeaf-spot 4 1 2 1 0 3
## 223 alternarialeaf-spot 6 0 2 1 0 1
## 224 alternarialeaf-spot 5 0 2 2 0 2
## 225 alternarialeaf-spot 5 1 2 1 0 0
## 226 alternarialeaf-spot 4 0 2 1 0 2
## 227 alternarialeaf-spot 4 0 2 1 0 1
## 228 alternarialeaf-spot 5 0 2 1 0 2
## 229 alternarialeaf-spot 6 0 2 2 0 3
## 230 alternarialeaf-spot 6 1 2 2 0 1
## 231 alternarialeaf-spot 5 1 2 2 0 3
## 232 alternarialeaf-spot 5 1 2 2 0 3
## 233 alternarialeaf-spot 4 1 2 1 0 2
## 234 alternarialeaf-spot 6 1 1 2 0 2
## 235 alternarialeaf-spot 4 1 2 1 0 1
## 236 alternarialeaf-spot 6 1 2 2 0 2
## 237 alternarialeaf-spot 4 1 2 1 0 0
## 238 alternarialeaf-spot 4 0 2 2 0 3
## 239 alternarialeaf-spot 5 0 2 2 0 2
## 240 alternarialeaf-spot 3 0 2 1 0 0
## 241 alternarialeaf-spot 5 0 2 1 0 1
## 242 alternarialeaf-spot 5 0 2 2 0 1
## 243 alternarialeaf-spot 4 0 2 2 0 1
## 244 alternarialeaf-spot 5 1 2 1 0 3
## 245 alternarialeaf-spot 6 0 2 1 0 2
## 246 alternarialeaf-spot 5 0 2 1 0 0
## 247 alternarialeaf-spot 6 0 2 1 0 0
## 248 alternarialeaf-spot 5 1 2 2 0 2
## 249 alternarialeaf-spot 5 0 2 1 0 3
## 250 alternarialeaf-spot 6 0 2 1 0 1
## 251 frog-eye-leaf-spot 6 0 1 2 0 3
## 252 frog-eye-leaf-spot 4 0 1 2 0 1
## 253 frog-eye-leaf-spot 5 0 1 1 0 2
## 254 frog-eye-leaf-spot 5 1 2 1 0 3
## 255 frog-eye-leaf-spot 6 1 2 2 0 3
## 256 frog-eye-leaf-spot 4 0 1 1 0 3
## 257 frog-eye-leaf-spot 3 0 2 1 0 2
## 258 frog-eye-leaf-spot 5 0 2 2 0 2
## 259 frog-eye-leaf-spot 5 0 2 1 0 1
## 260 frog-eye-leaf-spot 5 0 2 2 0 2
## 261 frog-eye-leaf-spot 5 0 2 1 0 0
## 262 frog-eye-leaf-spot 4 0 2 1 0 2
## 263 frog-eye-leaf-spot 4 0 2 2 0 1
## 264 frog-eye-leaf-spot 4 0 2 1 0 2
## 265 frog-eye-leaf-spot 3 1 2 1 0 3
## 266 frog-eye-leaf-spot 5 0 2 1 0 3
## 267 frog-eye-leaf-spot 5 0 2 2 0 1
## 268 frog-eye-leaf-spot 4 0 2 2 0 1
## 269 frog-eye-leaf-spot 5 0 2 2 0 2
## 270 frog-eye-leaf-spot 5 0 2 1 0 3
## 271 frog-eye-leaf-spot 3 0 2 1 0 1
## 272 frog-eye-leaf-spot 6 0 1 2 0 3
## 273 frog-eye-leaf-spot 5 0 1 1 0 1
## 274 frog-eye-leaf-spot 5 0 2 1 0 3
## 275 frog-eye-leaf-spot 5 1 2 1 0 3
## 276 frog-eye-leaf-spot 3 1 2 1 0 3
## 277 frog-eye-leaf-spot 6 1 2 2 0 3
## 278 frog-eye-leaf-spot 4 0 2 1 0 1
## 279 frog-eye-leaf-spot 4 0 2 2 0 1
## 280 frog-eye-leaf-spot 6 1 2 2 0 3
## 281 frog-eye-leaf-spot 5 1 2 2 0 3
## 282 frog-eye-leaf-spot 4 0 2 1 0 0
## 283 frog-eye-leaf-spot 4 0 2 1 0 2
## 284 frog-eye-leaf-spot 4 1 1 2 0 1
## 285 frog-eye-leaf-spot 4 0 2 1 0 2
## 286 frog-eye-leaf-spot 5 1 2 1 0 1
## 287 frog-eye-leaf-spot 4 0 2 2 0 1
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## leaf.mild_imp stem_imp lodging_imp stem.cankers_imp canker.lesion_imp
## 1 FALSE FALSE FALSE FALSE FALSE
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## 647 FALSE FALSE FALSE FALSE FALSE
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## 650 TRUE FALSE TRUE FALSE FALSE
## 651 TRUE FALSE TRUE FALSE FALSE
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## 655 TRUE FALSE TRUE FALSE FALSE
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## 660 TRUE FALSE TRUE TRUE TRUE
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## 666 TRUE TRUE TRUE TRUE TRUE
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## 675 TRUE TRUE TRUE TRUE TRUE
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## 678 TRUE TRUE TRUE TRUE TRUE
## 679 TRUE TRUE TRUE TRUE TRUE
## 680 TRUE FALSE TRUE TRUE TRUE
## 681 TRUE FALSE TRUE TRUE TRUE
## 682 TRUE FALSE TRUE TRUE TRUE
## 683 TRUE FALSE TRUE TRUE TRUE
## fruiting.bodies_imp ext.decay_imp mycelium_imp int.discolor_imp
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE
## 7 FALSE FALSE FALSE FALSE
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## 680 TRUE TRUE TRUE FALSE
## 681 TRUE TRUE TRUE FALSE
## 682 TRUE TRUE TRUE FALSE
## 683 TRUE TRUE TRUE FALSEtrans <- preProcess(soybeanTest, method = c("BoxCox", "center", "scale", "pca"))## Warning in pre_process_options(method, column_types): The following
## pre-processing methods were eliminated: 'BoxCox', 'center', 'scale', 'pca'trans## Created from 683 samples and 71 variables
##
## Pre-processing:
## - ignored (71)transformed <- predict(trans, soybeanTest)
head(transformed[, 1:5])## Class date plant.stand precip temp
## 1 diaporthe-stem-canker 6 0 2 1
## 2 diaporthe-stem-canker 4 0 2 1
## 3 diaporthe-stem-canker 3 0 2 1
## 4 diaporthe-stem-canker 3 0 2 1
## 5 diaporthe-stem-canker 6 0 2 1
## 6 diaporthe-stem-canker 5 0 2 1