Random Forest Classification of Mushrooms

There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random forest, which is a good place to start.

This is a use case in R of the randomForest package used on a data set from UCI’s Machine Learning Data Repository.

Are These Mushrooms Edible?

If someone gave you thousands of rows of data with dozens of columns about mushrooms, could you identify which characteristics make a mushroom edible or poisonous? How much would you trust your model? Would it be enough for you to make a decision on whether or not to eat a mushroom you find? (That’s a bad decision roughly 100% of the time).

The randomForest package does all of the heavy liftingbehind the scenes. While this “magic” is incredibly nice for the end user, it’s important to understand what it is you’re doing. Keep this in mind for absolutely any package you use in R or any other language.

“To know how to run these programs is impressive, but to truly understand how and why they work is what makes you an expert!” -Haley Stoltzman (my wife is a genius)

Here is an article which explains things in layman’s terms – A Gentle Introduction to Random Forests, Ensembles, and Performance Metrics in a Commercial System.

I created a function to grab and clean up the data. This happened to be a very manual process so I borrowed a lot of the code from others. Later on, I found that the data set had already been cleaned up by someone else and presented as a .csv file, but I decided to use my function anyway.

source('helper_functions.R')  
library(randomForest)  
library(e1071)  
library(caret)  
library(ggplot2)  
set.seed(123)  

I brought the data in as a dataframe, the first column is “Edible” which could be labeled “Class” as this is what we’re looking for in the classification. We’ll find only two values here, “Edible” and “Poisonous” (keep in mind that more than two values are easily handled by random forest).

I printed the first few rows and the output shows us there are 23 columns (including “Edible”). I am not a mushroom expert but most of this data makes sense to try and utilize.

#Import Data via Custom Function
data = fetchAndCleanData()  
head(data)  
##      Edible CapShape CapSurface CapColor Bruises    Odor GillAttachment
## 1 Poisonous   Convex     Smooth    Brown    True Pungent           Free
## 2    Edible   Convex     Smooth   Yellow    True  Almond           Free
## 3    Edible     Bell     Smooth    White    True   Anise           Free
## 4 Poisonous   Convex      Scaly    White    True Pungent           Free
## 5    Edible   Convex     Smooth     Gray   False    None           Free
## 6    Edible   Convex      Scaly   Yellow    True  Almond           Free
##   GillSpacing GillSize GillColor StalkShape StalkRoot
## 1       Close   Narrow     Black  Enlarging     Equal
## 2       Close    Broad     Black  Enlarging      Club
## 3       Close    Broad     Brown  Enlarging      Club
## 4       Close   Narrow     Brown  Enlarging     Equal
## 5     Crowded    Broad     Black   Tapering     Equal
## 6       Close    Broad     Brown  Enlarging      Club
##   StalkSurfaceAboveRing StalkSurfaceBelowRing StalkColorAboveRing
## 1                Smooth                Smooth               White
## 2                Smooth                Smooth               White
## 3                Smooth                Smooth               White
## 4                Smooth                Smooth               White
## 5                Smooth                Smooth               White
## 6                Smooth                Smooth               White
##   StalkColorBelowRing VeilType VeilColor RingNumber   RingType
## 1               White  Partial     White        One    Pendant
## 2               White  Partial     White        One    Pendant
## 3               White  Partial     White        One    Pendant
## 4               White  Partial     White        One    Pendant
## 5               White  Partial     White        One Evanescent
## 6               White  Partial     White        One    Pendant
##   SporePrintColor Population Habitat
## 1           Black  Scattered   Urban
## 2           Brown   Numerous Grasses
## 3           Brown   Numerous Meadows
## 4           Black  Scattered   Urban
## 5           Brown  Abundnant Grasses
## 6           Black   Numerous Grasses

It’s important to know that R’s random forest package cannot use rows with missing data. Using the summary() function can help to identify issues. This data doesn’t have missing information.

summary(data) #no missing data appears  
##        Edible        CapShape      CapSurface      CapColor   
##  Edible   :4208   Convex :3656   Scaly  :3244   Brown  :2284  
##  Poisonous:3916   Flat   :3152   Smooth :2556   Gray   :1840  
##                   Knobbed: 828   Fibrous:2320   Red    :1500  
##                   Bell   : 452   Grooves:   4   Yellow :1072  
##                   Sunken :  32   f      :   0   White  :1040  
##                   Conical:   4   g      :   0   Buff   : 168  
##                   (Other):   0   (Other):   0   (Other): 220  
##   Bruises          Odor         GillAttachment  GillSpacing  
##  f    :   0   None   :3528   a         :   0   c      :   0  
##  t    :   0   Foul   :2160   f         :   0   w      :   0  
##  True :3376   Fishy  : 576   Attached  : 210   Close  :6812  
##  False:4748   Spicy  : 576   Descending:   0   Crowded:1312  
##               Almond : 400   Free      :7914   Distant:   0  
##               Anise  : 400   Notched   :   0                 
##               (Other): 484                                   
##    GillSize        GillColor        StalkShape     StalkRoot   
##  b     :   0   Buff     :1728   e        :   0   Bulbous:3776  
##  n     :   0   Pink     :1492   t        :   0   Missing:2480  
##  Broad :5612   White    :1202   Enlarging:3516   Equal  :1120  
##  Narrow:2512   Brown    :1048   Tapering :4608   Club   : 556  
##                Gray     : 752                    Rooted : 192  
##                Chocolate: 732                    ?      :   0  
##                (Other)  :1170                    (Other):   0  
##  StalkSurfaceAboveRing StalkSurfaceBelowRing StalkColorAboveRing
##  Smooth :5176          Smooth :4936          White  :4464       
##  Silky  :2372          Silky  :2304          Pink   :1872       
##  Fibrous: 552          Fibrous: 600          Gray   : 576       
##  Scaly  :  24          Scaly  : 284          Brown  : 448       
##  f      :   0          f      :   0          Buff   : 432       
##  k      :   0          k      :   0          Orange : 192       
##  (Other):   0          (Other):   0          (Other): 140       
##  StalkColorBelowRing      VeilType      VeilColor    RingNumber 
##  White  :4384        p        :   0   White  :7924   n   :   0  
##  Pink   :1872        Partial  :8124   Brown  :  96   o   :   0  
##  Gray   : 576        Universal:   0   Orange :  96   t   :   0  
##  Brown  : 512                         Yellow :   8   None:  36  
##  Buff   : 432                         n      :   0   One :7488  
##  Orange : 192                         o      :   0   Two : 600  
##  (Other): 156                         (Other):   0              
##        RingType     SporePrintColor     Population      Habitat    
##  Pendant   :3968   White    :2388   Several  :4040   Woods  :3148  
##  Evanescent:2776   Brown    :1968   Solitary :1712   Grasses:2148  
##  Large     :1296   Black    :1872   Scattered:1248   Paths  :1144  
##  Flaring   :  48   Chocolate:1632   Numerous : 400   Leaves : 832  
##  None      :  36   Green    :  72   Abundnant: 384   Urban  : 368  
##  e         :   0   Buff     :  48   Clustered: 340   Meadows: 292  
##  (Other)   :   0   (Other)  : 144   (Other)  :   0   (Other): 192

I want to explore the data before fitting a model to get an idea of what to expect. I am plotting a variable on two axes and using colors to see the relationship as to whether or not the mushroom is edible or poisonous.

In these plots, edible is shown as green and poisonous is shown as red. I’m looking for spots where there exists an overwhelming majority of one color.

A comparison of “CapSurface” to “CapShape” shows us:

  • CapShape Bell is more likely to be edible
  • CapShape Convex or Flat have a mix of edible and poisonous and make up the majority of the data
  • CapSurface alone does not tell us a lot of information
  • CapSurface Fibrous + CapShape Bell, Knobbed, or Sunken are likely to be edible
  • These variables will likely increase information gain but may not be incredibly strong

plot

p = ggplot(data,aes(x=CapShape,  
                    y=CapSurface, 
                    color=Edible))

p + geom_jitter(alpha=0.3) +  
  scale_color_manual(breaks = c('Edible','Poisonous'),
                     values=c('darkgreen','red'))

A comparison of “StalkColorBelowRing” to “StalkColorAboveRing” shows us:

  • StalkColorAboveRing Gray is almost always going to be edible
  • StalkColorBelowRing Gray is almost always going to be edible
  • StalkColorBelowRing Buff is almost always going to be poisonous
  • This list could go on…
  • These variables are likely to increase information gain by a fair amount

plot

p = ggplot(data,aes(x=StalkColorBelowRing,  
                    y=StalkColorAboveRing,
                    color=Edible))

p + geom_jitter(alpha=0.3) +  
  scale_color_manual(breaks = c('Edible','Poisonous'),
                     values=c('darkgreen','red'))

A comparison of “Odor” to “SporePrintColor” shows us:

  • Odor Foul, Fishy, Pungent, Creosote, and Spicy are highly likely to be poisonous
  • Odor Almond and Anise are highly likely to be edible.
  • Odor None appears to be primarily edible
    • However, if it has SporePrintColor Green it is highly likely to be poisonous!
  • These variables are likely going to lead to a lot of information gain

plot

p = ggplot(data,aes(x=Odor,  
                    y=SporePrintColor, 
                    color=Edible))

p + geom_jitter(alpha=0.3) +  
  scale_color_manual(breaks = c('Edible','Poisonous'),
                     values=c('darkgreen','red'))

Due to how strong those variables looked, I decided to plot them strictly as edible or poisonous and found:

  • Odor is an excellent indicator of edible or poisonous
  • Odor None is the only tricky one – there is data where it would be classified as edible or poisonous
  • SporePrintColor is not as strong as odor when it stands alone – there is a lot of overlap between the columns

plot

plot

p = ggplot(data,aes(x=Edible,  
                    y=Odor, 
                    color = Edible))

p + geom_jitter(alpha=0.2) +  
  scale_color_manual(breaks = c('Edible','Poisonous'),
                     values=c('darkgreen','red'))
p = ggplot(data,aes(x=Edible,  
                    y=SporePrintColor, 
                    color = Edible))

p + geom_jitter(alpha=0.2) +  
  scale_color_manual(breaks = c('Edible','Poisonous'),
                     values=c('darkgreen','red'))

Before fitting a model it’s important to split data into different parts – train and test data. There’s no perfect way to know exactly how much data you should use to train your model. In this example I split 5% as training and 95% as testing. However, this is not typical, most of what I see is usually around 60%/40% or 70%/30% for test/train split.

If you choose too large of a training set you run the risk of overfitting your model. Overfitting is a classic mistake people make when first entering the field of machine learning. I won’t go into the details but there are classes dedicated to this subject. Wikipedia Article

Initially, I ran this at higher levels of training data and it had perfect prediction with zero false positives or negatives. That’s not as fun to look at as an example so I scaled down the training data which created more bad predictions.

#Create data for training
sample.ind = sample(2,  
                     nrow(data),
                     replace = T,
                     prob = c(0.05,0.95))
data.dev = data[sample.ind==1,]  
data.val = data[sample.ind==2,]  

I wanted to know the split of edible to poisonous mushrooms in the data set and compare it to the training and test data. The random sample appears to have created roughly the same ratio of edible to poisonous upon creating train and test data.

Edible % / Poisonous % :

  • Data: 52 / 48
  • Train: 50 / 50
  • Test: 52 / 48
# Original Data
table(data$Edible)/nrow(data)  
##    Edible Poisonous 
## 0.5179714 0.4820286
# Training Data
table(data.dev$Edible)/nrow(data.dev)  
##    Edible Poisonous 
## 0.4962779 0.5037221
# Testing Data
table(data.val$Edible)/nrow(data.val)  
##    Edible Poisonous 
## 0.5191037 0.4808963

plot

I finally fit the random forest model to the training data. Plotting the model shows us that after about 20 trees, not much changes in terms of error. It fluctuates a bit but not to a large degree.

#Fit Random Forest Model
rf = randomForest(Edible ~ .,  
                   ntree = 100,
                   data = data.dev)
plot(rf)  

Printing the model shows the number of variables tried at each split to be 4 and an OOB estimate of error rate 0.25%. The training model fit the training data almost perfectly. There was only one mushroom which was classified incorrectly. The model would have predicted 1 to be poisonous and it would have turned out to be edible. If we consider edible to be “positive” this means we would have had 1 false negative.

print(rf)  
## Call:
##  randomForest(formula = Edible ~ ., data = data.dev, ntree = 100) 
##                Type of random forest: classification
##                      Number of trees: 100
## No. of variables tried at each split: 4
## 
##         OOB estimate of  error rate: 0.25%
## Confusion matrix:
##           Edible Poisonous class.error
## Edible       200         0 0.000000000
## Poisonous      1       202 0.004926108

It’s always important to look at what is shown in terms of variable importance. This plot indicates what variables had the greatest impact in the classification model.

I limited it to 10 for the plot.

plot

# Variable Importance
varImpPlot(rf,  
           sort = T,
           n.var=10,
           main="Top 10 - Variable Importance")

Odor is by far the most important variable in terms of “Mean Decreasing Gini” – a similar term for information gain in this example. The rest of the results are listed below. It’s interesting to notice “Veil Type” created no information gain – so I looked into it in the initial data. The reason is clear – there is only one VeilType, so it doesn’t offer any differentiation and couldn’t possibly impact the results.

#Variable Importance
var.imp = data.frame(importance(rf,  
                                 type=2))
# make row names as columns
var.imp$Variables = row.names(var.imp)  
print(var.imp[order(var.imp$MeanDecreaseGini,decreasing = T),])

##                       MeanDecreaseGini             Variables
## Odor                        69.3536782                  Odor
## SporePrintColor             27.3837625       SporePrintColor
## GillColor                   18.1981987             GillColor
## StalkSurfaceAboveRing       12.3172400 StalkSurfaceAboveRing
## RingType                    11.3114967              RingType
## GillSize                    11.1085947              GillSize
## Population                   7.2591707            Population
## Bruises                      7.2212660               Bruises
## CapColor                     5.6746095              CapColor
## Habitat                      5.4768013               Habitat
## StalkRoot                    5.3053036             StalkRoot
## StalkSurfaceBelowRing        4.6080070 StalkSurfaceBelowRing
## GillSpacing                  4.1186021           GillSpacing
## StalkShape                   2.6858568            StalkShape
## StalkColorBelowRing          2.5570551   StalkColorBelowRing
## RingNumber                   2.0463027            RingNumber
## StalkColorAboveRing          1.9823127   StalkColorAboveRing
## CapSurface                   1.0200298            CapSurface
## CapShape                     0.5779989              CapShape
## VeilColor                    0.1522645             VeilColor
## GillAttachment               0.0275000        GillAttachment
## VeilType                     0.0000000              VeilType

I decided to use the model to attempt to predict whether or not a mushroom is edible or poisonous based off of the training data set. It predicted the response variable perfectly – having zero false positives or false negatives.

# Predicting response variable
data.dev$predicted.response = predict(rf , data.dev)

# Create Confusion Matrix
print(  
confusionMatrix(data = data.dev$predicted.response,  
                reference = data.dev$Edible,
                positive = 'Edible'))

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  Edible Poisonous
##   Edible       200         0
##   Poisonous      0       203
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9909, 1)
##     No Information Rate : 0.5037     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.4963     
##          Detection Rate : 0.4963     
##    Detection Prevalence : 0.4963     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : Edible     

Now it was time to see how the model did with data it had not seen before – making predictions on the test data.

It did a decent job. It had a 99% accuracy with a very narrow confidence interval. It did have 48 false negatives and 8 false positives (which could be deadly if you were actually choosing to eat mushrooms based off of this model).

# Predicting response variable
data.val$predicted.response <- predict(rf ,data.val)

# Create Confusion Matrix
print(  
confusionMatrix(data=data.val$predicted.response,  
                reference=data.val$Edible,
                positive='Edible'))

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  Edible Poisonous
##   Edible      3960         8
##   Poisonous     48      3705
##                                           
##                Accuracy : 0.9927          
##                  95% CI : (0.9906, 0.9945)
##     No Information Rate : 0.5191          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.9855          
##  Mcnemar's Test P-Value : 1.872e-07       
##                                           
##             Sensitivity : 0.9880          
##             Specificity : 0.9978          
##          Pos Pred Value : 0.9980          
##          Neg Pred Value : 0.9872          
##              Prevalence : 0.5191          
##          Detection Rate : 0.5129          
##    Detection Prevalence : 0.5139          
##       Balanced Accuracy : 0.9929          
##                                           
##        'Positive' Class : Edible    

Unfortunately, I have no idea how reliable this data is or how it was captured. There is likely some background information and I would never choose whether or not to eat an unknown mushroom based off of this model (and neither should you).

Code used in this post is on my GitHub