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#### Part 2: Rule Learners -------------------
## Example: Identifying Poisonous Mushrooms ----
## Step 2: Exploring and preparing the data ---- 
mushrooms <- read.csv("mushrooms.csv", stringsAsFactors = TRUE)
# examine the structure of the data frame
str(mushrooms)

'data.frame':   8124 obs. of  23 variables:
 $ type                    : Factor w/ 2 levels "edible","poisonous": 2 1 1 2 1 1 1 1 2 1 ...
 $ cap_shape               : Factor w/ 6 levels "bell","conical",..: 3 3 1 3 3 3 1 1 3 1 ...
 $ cap_surface             : Factor w/ 4 levels "fibrous","grooves",..: 4 4 4 3 4 3 4 3 3 4 ...
 $ cap_color               : Factor w/ 10 levels "brown","buff",..: 1 10 9 9 4 10 9 9 9 10 ...
 $ bruises                 : Factor w/ 2 levels "no","yes": 2 2 2 2 1 2 2 2 2 2 ...
 $ odor                    : Factor w/ 9 levels "almond","anise",..: 8 1 2 8 7 1 1 2 8 1 ...
 $ gill_attachment         : Factor w/ 2 levels "attached","free": 2 2 2 2 2 2 2 2 2 2 ...
 $ gill_spacing            : Factor w/ 2 levels "close","crowded": 1 1 1 1 2 1 1 1 1 1 ...
 $ gill_size               : Factor w/ 2 levels "broad","narrow": 2 1 1 2 1 1 1 1 2 1 ...
 $ gill_color              : Factor w/ 12 levels "black","brown",..: 1 1 2 2 1 2 5 2 8 5 ...
 $ stalk_shape             : Factor w/ 2 levels "enlarging","tapering": 1 1 1 1 2 1 1 1 1 1 ...
 $ stalk_root              : Factor w/ 5 levels "bulbous","club",..: 3 2 2 3 3 2 2 2 3 2 ...
 $ stalk_surface_above_ring: Factor w/ 4 levels "fibrous","scaly",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ stalk_surface_below_ring: Factor w/ 4 levels "fibrous","scaly",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ stalk_color_above_ring  : Factor w/ 9 levels "brown","buff",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ stalk_color_below_ring  : Factor w/ 9 levels "brown","buff",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ veil_type               : Factor w/ 1 level "partial": 1 1 1 1 1 1 1 1 1 1 ...
 $ veil_color              : Factor w/ 4 levels "brown","orange",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ ring_number             : Factor w/ 3 levels "none","one","two": 2 2 2 2 2 2 2 2 2 2 ...
 $ ring_type               : Factor w/ 5 levels "evanescent","flaring",..: 5 5 5 5 1 5 5 5 5 5 ...
 $ spore_print_color       : Factor w/ 9 levels "black","brown",..: 1 2 2 1 2 1 1 2 1 1 ...
 $ population              : Factor w/ 6 levels "abundant","clustered",..: 4 3 3 4 1 3 3 4 5 4 ...
 $ habitat                 : Factor w/ 7 levels "grasses","leaves",..: 5 1 3 5 1 1 3 3 1 3 ...

# drop the veil_type feature
mushrooms$veil_type <- NULL
# examine the class distribution
table(mushrooms$type)

   edible poisonous 
     4208      3916 

set.seed(123)
train_sample <- sample(8124, 7000)
str(train_sample)

 int [1:7000] 2337 6404 3322 7171 7637 370 4288 7244 4476 3706 ...

# split the data frames
mushrooms_train <- mushrooms[train_sample, ]
mushrooms_test  <- mushrooms[-train_sample, ]
## Step 3: Training a model on the data ----
library(RWeka)
# train OneR() on the data
mushroom_1R <- OneR(type ~ ., data = mushrooms_train)
## Step 4: Evaluating model performance ----
mushroom_1R

odor:
    almond  -> edible
    anise   -> edible
    creosote    -> poisonous
    fishy   -> poisonous
    foul    -> poisonous
    musty   -> poisonous
    none    -> edible
    pungent -> poisonous
    spicy   -> poisonous
(6895/7000 instances correct)

summary(mushroom_1R)

=== Summary ===

Correctly Classified Instances        6895               98.5    %
Incorrectly Classified Instances       105                1.5    %
Kappa statistic                          0.9699
Mean absolute error                      0.015 
Root mean squared error                  0.1225
Relative absolute error                  3.0039 %
Root relative squared error             24.5108 %
Total Number of Instances             7000     

=== Confusion Matrix ===

    a    b   <-- classified as
 3626    0 |    a = edible
  105 3269 |    b = poisonous

mushroom_pred <- predict(mushroom_1R, mushrooms_test)
# cross tabulation of predicted versus actual classes
library(gmodels)
CrossTable(mushrooms_test$type, mushroom_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('actual default', 'predicted default'))

 
   Cell Contents
|-------------------------|
|                       N |
|         N / Table Total |
|-------------------------|

 
Total Observations in Table:  1124 

 
               | predicted default 
actual default |    edible | poisonous | Row Total | 
---------------|-----------|-----------|-----------|
        edible |       582 |         0 |       582 | 
               |     0.518 |     0.000 |           | 
---------------|-----------|-----------|-----------|
     poisonous |        15 |       527 |       542 | 
               |     0.013 |     0.469 |           | 
---------------|-----------|-----------|-----------|
  Column Total |       597 |       527 |      1124 | 
---------------|-----------|-----------|-----------|

 

## Step 5: Improving model performance ----
mushroom_JRip <- JRip(type ~ ., data = mushrooms_train)
mushroom_JRip

JRIP rules:
===========

(odor = foul) => type=poisonous (1860.0/0.0)
(gill_size = narrow) and (gill_color = buff) => type=poisonous (986.0/0.0)
(gill_size = narrow) and (odor = pungent) => type=poisonous (222.0/0.0)
(odor = creosote) => type=poisonous (171.0/0.0)
(spore_print_color = green) => type=poisonous (65.0/0.0)
(stalk_surface_below_ring = scaly) and (stalk_surface_above_ring = silky) => type=poisonous (58.0/0.0)
(habitat = leaves) and (cap_surface = scaly) and (population = clustered) => type=poisonous (10.0/0.0)
(cap_surface = grooves) => type=poisonous (2.0/0.0)
 => type=edible (3626.0/0.0)

Number of Rules : 9

summary(mushroom_JRip)

=== Summary ===

Correctly Classified Instances        7000              100      %
Incorrectly Classified Instances         0                0      %
Kappa statistic                          1     
Mean absolute error                      0     
Root mean squared error                  0     
Relative absolute error                  0      %
Root relative squared error              0      %
Total Number of Instances             7000     

=== Confusion Matrix ===

    a    b   <-- classified as
 3626    0 |    a = edible
    0 3374 |    b = poisonous

mushroom_pred <- predict(mushroom_JRip, mushrooms_test)
# cross tabulation of predicted versus actual classes
library(gmodels)
CrossTable(mushrooms_test$type, mushroom_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('actual default', 'predicted default'))

 
   Cell Contents
|-------------------------|
|                       N |
|         N / Table Total |
|-------------------------|

 
Total Observations in Table:  1124 

 
               | predicted default 
actual default |    edible | poisonous | Row Total | 
---------------|-----------|-----------|-----------|
        edible |       582 |         0 |       582 | 
               |     0.518 |     0.000 |           | 
---------------|-----------|-----------|-----------|
     poisonous |         0 |       542 |       542 | 
               |     0.000 |     0.482 |           | 
---------------|-----------|-----------|-----------|
  Column Total |       582 |       542 |      1124 | 
---------------|-----------|-----------|-----------|

 

# Rule Learner Using C5.0 Decision Trees (not in text)
library(C50)
mushroom_c5rules <- C5.0(type ~ odor + gill_size, data = mushrooms_train, rules = TRUE)
mushroom_c5rules

Call:
C5.0.formula(formula = type ~ odor + gill_size, data = mushrooms_train, rules = TRUE)

Rule-Based Model
Number of samples: 7000 
Number of predictors: 2 

Number of Rules: 2 

Non-standard options: attempt to group attributes

summary(mushroom_c5rules)

Call:
C5.0.formula(formula = type ~ odor + gill_size, data = mushrooms_train, rules = TRUE)


C5.0 [Release 2.07 GPL Edition]     Wed May 03 17:27:26 2017
-------------------------------

Class specified by attribute `outcome'

Read 7000 cases (3 attributes) from undefined.data

Rules:

Rule 1: (3731/105, lift 1.9)
    odor in {almond, anise, none}
    ->  class edible  [0.972]

Rule 2: (3269, lift 2.1)
    odor in {creosote, fishy, foul, musty, pungent, spicy}
    ->  class poisonous  [1.000]

Default class: edible


Evaluation on training data (7000 cases):

            Rules     
      ----------------
        No      Errors

         2  105( 1.5%)   <<


       (a)   (b)    <-classified as
      ----  ----
      3626          (a): class edible
       105  3269    (b): class poisonous


    Attribute usage:

    100.00% odor


Time: 0.0 secs

mushroom_pred <- predict(mushroom_c5rules, mushrooms_test)
# cross tabulation of predicted versus actual classes
library(gmodels)
CrossTable(mushrooms_test$type, mushroom_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('actual default', 'predicted default'))

 
   Cell Contents
|-------------------------|
|                       N |
|         N / Table Total |
|-------------------------|

 
Total Observations in Table:  1124 

 
               | predicted default 
actual default |    edible | poisonous | Row Total | 
---------------|-----------|-----------|-----------|
        edible |       582 |         0 |       582 | 
               |     0.518 |     0.000 |           | 
---------------|-----------|-----------|-----------|
     poisonous |        15 |       527 |       542 | 
               |     0.013 |     0.469 |           | 
---------------|-----------|-----------|-----------|
  Column Total |       597 |       527 |      1124 | 
---------------|-----------|-----------|-----------|

 

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