我正在尝试为蘑菇数据构建一个简单的Naive Bayes分类器。我想用所有的变量作为分类预测因子来预测蘑菇是否可食用。
我正在使用插入符号包。
这是我的完整代码：
##################################################################################
# Prepare R and R Studio environment
##################################################################################
# Clear the R studio console
cat(" 14")
# Remove objects from environment
rm(list = ls())
# Install and load packages if necessary
if (!require(tidyverse)) {
install.packages("tidyverse")
library(tidyverse)
}
if (!require(caret)) {
install.packages("caret")
library(caret)
}
if (!require(klaR)) {
install.packages("klaR")
library(klaR)
}
#################################
mushrooms <- read.csv("agaricus-lepiota.data", stringsAsFactors = TRUE, header = FALSE)
na.omit(mushrooms)
names(mushrooms) <- c("edibility", "capShape", "capSurface", "cap-color", "bruises", "odor", "gill-attachment", "gill-spacing", "gill-size", "gill-color", "stalk-shape", "stalk-root", "stalk-surface-above-ring", "stalk-surface-below-ring", "stalk-color-above-ring", "stalk-color-below-ring", "veil-type", "veil-color", "ring-number", "ring-type", "spore-print-color", "population", "habitat")
# convert bruises to a logical variable
mushrooms$bruises <- mushrooms$bruises == 't'
set.seed(1234)
split <- createDataPartition(mushrooms$edibility, p = 0.8, list = FALSE)
train <- mushrooms[split, ]
test <- mushrooms[-split, ]
predictors <- names(train)[2:20] #Create response and predictor data
x <- train[,predictors] #predictors
y <- train$edibility #response
train_control <- trainControl(method = "cv", number = 1) # Set up 1 fold cross validation
edibility_mod1 <- train( #train the model
x = x,
y = y,
method = "nb",
trControl = train_control
)
当执行train((函数时，我得到以下输出：
Something is wrong; all the Accuracy metric values are missing:
Accuracy Kappa
Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA
Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA
NA's :2 NA's :2
Error: Stopping
In addition: Warning messages:
1: predictions failed for Fold1: usekernel= TRUE, fL=0, adjust=1 Error in predict.NaiveBayes(modelFit, newdata) :
Not all variable names used in object found in newdata
2: model fit failed for Fold1: usekernel=FALSE, fL=0, adjust=1 Error in x[, 2] : subscript out of bounds
3: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures.
脚本运行后的x和y：
> str(x)
'data.frame': 6500 obs. of 19 variables:
$ capShape : Factor w/ 6 levels "b","c","f","k",..: 6 6 1 6 6 6 1 1 6 1 ...
$ capSurface : Factor w/ 4 levels "f","g","s","y": 3 3 3 4 3 4 3 4 4 3 ...
$ cap-color : Factor w/ 10 levels "b","c","e","g",..: 5 10 9 9 4 10 9 9 9 10 ...
$ bruises : logi TRUE TRUE TRUE TRUE FALSE TRUE ...
$ odor : Factor w/ 9 levels "a","c","f","l",..: 7 1 4 7 6 1 1 4 7 1 ...
$ gill-attachment : Factor w/ 2 levels "a","f": 2 2 2 2 2 2 2 2 2 2 ...
$ gill-spacing : Factor w/ 2 levels "c","w": 1 1 1 1 2 1 1 1 1 1 ...
$ gill-size : Factor w/ 2 levels "b","n": 2 1 1 2 1 1 1 1 2 1 ...
$ gill-color : Factor w/ 12 levels "b","e","g","h",..: 5 5 6 6 5 6 3 6 8 3 ...
$ stalk-shape : Factor w/ 2 levels "e","t": 1 1 1 1 2 1 1 1 1 1 ...
$ stalk-root : Factor w/ 5 levels "?","b","c","e",..: 4 3 3 4 4 3 3 3 4 3 ...
$ stalk-surface-above-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ stalk-surface-below-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ stalk-color-above-ring : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ stalk-color-below-ring : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ veil-type : Factor w/ 1 level "p": 1 1 1 1 1 1 1 1 1 1 ...
$ veil-color : Factor w/ 4 levels "n","o","w","y": 3 3 3 3 3 3 3 3 3 3 ...
$ ring-number : Factor w/ 3 levels "n","o","t": 2 2 2 2 2 2 2 2 2 2 ...
$ ring-type : Factor w/ 5 levels "e","f","l","n",..: 5 5 5 5 1 5 5 5 5 5 ...
> str(y)
Factor w/ 2 levels "e","p": 2 1 1 2 1 1 1 1 2 1 ...
我的环境是：
> R.version
_
platform x86_64-apple-darwin17.0
arch x86_64
os darwin17.0
system x86_64, darwin17.0
status
major 4
minor 0.3
year 2020
month 10
day 10
svn rev 79318
language R
version.string R version 4.0.3 (2020-10-10)
nickname Bunny-Wunnies Freak Out
> RStudio.Version()
$citation
To cite RStudio in publications use:
RStudio Team (2020). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
A BibTeX entry for LaTeX users is
@Manual{,
title = {RStudio: Integrated Development Environment for R},
author = {{RStudio Team}},
organization = {RStudio, PBC},
address = {Boston, MA},
year = {2020},
url = {http://www.rstudio.com/},
}
$mode
[1] "desktop"
$version
[1] ‘1.3.1093’
$release_name
[1] "Apricot Nasturtium"
您试图做的是一个有点棘手的、最天真的bayes实现，或者至少您正在使用的实现(来自从e1071派生的kLAR(使用正态分布。您可以在e1071:的naiveBayes帮助页面的详细信息下看到
标准的朴素贝叶斯分类器(至少在这个实现中(假设预测变量独立，并且高斯度量预测器的分布(给定目标类别(。对于缺少值的属性，相应的表条目为为了预测而省略。
而且你的预测因子是分类的，所以这可能有问题。您可以尝试设置kernel=TRUE和adjust=1以强制其正常，并避免kernel=FALSE会引发错误。
在此之前，我们删除只有1级的列，并对列名进行排序，在这种情况下，使用公式更容易，避免生成伪变量：
df = train
levels(df[["veil-type"]])
[1] "p"
df[["veil-type"]]=NULL
colnames(df) = gsub("-","_",colnames(df))
Grid = expand.grid(usekernel=TRUE,adjust=1,fL=c(0.2,0.5,0.8))
mod1 <- train(edibility~.,data=df,
method = "nb", trControl = trainControl(method="cv",number=5),
tuneGrid=Grid
)
mod1
Naive Bayes
6500 samples
21 predictor
2 classes: 'e', 'p'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 5200, 5200, 5200, 5200, 5200
Resampling results across tuning parameters:
fL Accuracy Kappa
0.2 0.9243077 0.8478624
0.5 0.9243077 0.8478624
0.8 0.9243077 0.8478624
Tuning parameter 'usekernel' was held constant at a value of TRUE
Tuning parameter 'adjust' was held constant at a value of 1
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were fL = 0.2, usekernel = TRUE and
adjust = 1.
而不是使用"nb"；在列车功能中。使用"；naive_bayes"；。即使有严重的阶级失衡问题，它也对我有效。