22 Detect mines vs rocks with Random Forest

Datasets: Sonar
Algorithms:
- Random Forest

22.1 Introduction

mtry: Number of variables randomly sampled as candidates at each split.
ntree: Number of trees to grow.

22.2 Load libraries

# load packages
library(caret)
library(mlbench)
library(randomForest)
library(tictoc)

# load dataset
data(Sonar)
set.seed(7)

22.3 Explore data

dplyr::glimpse(Sonar)
#> Rows: 208
#> Columns: 61
#> $ V1    <dbl> 0.0200, 0.0453, 0.0262, 0.0100, 0.0762, 0.0286, 0.0317, 0.0519,…
#> $ V2    <dbl> 0.0371, 0.0523, 0.0582, 0.0171, 0.0666, 0.0453, 0.0956, 0.0548,…
#> $ V3    <dbl> 0.0428, 0.0843, 0.1099, 0.0623, 0.0481, 0.0277, 0.1321, 0.0842,…
#> $ V4    <dbl> 0.0207, 0.0689, 0.1083, 0.0205, 0.0394, 0.0174, 0.1408, 0.0319,…
#> $ V5    <dbl> 0.0954, 0.1183, 0.0974, 0.0205, 0.0590, 0.0384, 0.1674, 0.1158,…
#> $ V6    <dbl> 0.0986, 0.2583, 0.2280, 0.0368, 0.0649, 0.0990, 0.1710, 0.0922,…
#> $ V7    <dbl> 0.1539, 0.2156, 0.2431, 0.1098, 0.1209, 0.1201, 0.0731, 0.1027,…
#> $ V8    <dbl> 0.1601, 0.3481, 0.3771, 0.1276, 0.2467, 0.1833, 0.1401, 0.0613,…
#> $ V9    <dbl> 0.3109, 0.3337, 0.5598, 0.0598, 0.3564, 0.2105, 0.2083, 0.1465,…
#> $ V10   <dbl> 0.2111, 0.2872, 0.6194, 0.1264, 0.4459, 0.3039, 0.3513, 0.2838,…
#> $ V11   <dbl> 0.1609, 0.4918, 0.6333, 0.0881, 0.4152, 0.2988, 0.1786, 0.2802,…
#> $ V12   <dbl> 0.1582, 0.6552, 0.7060, 0.1992, 0.3952, 0.4250, 0.0658, 0.3086,…
#> $ V13   <dbl> 0.2238, 0.6919, 0.5544, 0.0184, 0.4256, 0.6343, 0.0513, 0.2657,…
#> $ V14   <dbl> 0.0645, 0.7797, 0.5320, 0.2261, 0.4135, 0.8198, 0.3752, 0.3801,…
#> $ V15   <dbl> 0.0660, 0.7464, 0.6479, 0.1729, 0.4528, 1.0000, 0.5419, 0.5626,…
#> $ V16   <dbl> 0.2273, 0.9444, 0.6931, 0.2131, 0.5326, 0.9988, 0.5440, 0.4376,…
#> $ V17   <dbl> 0.3100, 1.0000, 0.6759, 0.0693, 0.7306, 0.9508, 0.5150, 0.2617,…
#> $ V18   <dbl> 0.300, 0.887, 0.755, 0.228, 0.619, 0.902, 0.426, 0.120, 0.380, …
#> $ V19   <dbl> 0.508, 0.802, 0.893, 0.406, 0.203, 0.723, 0.202, 0.668, 0.740, …
#> $ V20   <dbl> 0.4797, 0.7818, 0.8619, 0.3973, 0.4636, 0.5122, 0.4233, 0.9402,…
#> $ V21   <dbl> 0.578, 0.521, 0.797, 0.274, 0.415, 0.207, 0.772, 0.783, 0.980, …
#> $ V22   <dbl> 0.507, 0.405, 0.674, 0.369, 0.429, 0.399, 0.974, 0.535, 0.889, …
#> $ V23   <dbl> 0.433, 0.396, 0.429, 0.556, 0.573, 0.589, 0.939, 0.681, 0.671, …
#> $ V24   <dbl> 0.555, 0.391, 0.365, 0.485, 0.540, 0.287, 0.556, 0.917, 0.429, …
#> $ V25   <dbl> 0.671, 0.325, 0.533, 0.314, 0.316, 0.204, 0.527, 0.761, 0.337, …
#> $ V26   <dbl> 0.641, 0.320, 0.241, 0.533, 0.229, 0.578, 0.683, 0.822, 0.737, …
#> $ V27   <dbl> 0.7104, 0.3271, 0.5070, 0.5256, 0.6995, 0.5389, 0.5713, 0.8872,…
#> $ V28   <dbl> 0.8080, 0.2767, 0.8533, 0.2520, 1.0000, 0.3750, 0.5429, 0.6091,…
#> $ V29   <dbl> 0.6791, 0.4423, 0.6036, 0.2090, 0.7262, 0.3411, 0.2177, 0.2967,…
#> $ V30   <dbl> 0.3857, 0.2028, 0.8514, 0.3559, 0.4724, 0.5067, 0.2149, 0.1103,…
#> $ V31   <dbl> 0.131, 0.379, 0.851, 0.626, 0.510, 0.558, 0.581, 0.132, 0.301, …
#> $ V32   <dbl> 0.2604, 0.2947, 0.5045, 0.7340, 0.5459, 0.4778, 0.6323, 0.0624,…
#> $ V33   <dbl> 0.512, 0.198, 0.186, 0.612, 0.288, 0.330, 0.296, 0.099, 0.317, …
#> $ V34   <dbl> 0.7547, 0.2341, 0.2709, 0.3497, 0.0981, 0.2198, 0.1873, 0.4006,…
#> $ V35   <dbl> 0.8537, 0.1306, 0.4232, 0.3953, 0.1951, 0.1407, 0.2969, 0.3666,…
#> $ V36   <dbl> 0.851, 0.418, 0.304, 0.301, 0.418, 0.286, 0.516, 0.105, 0.219, …
#> $ V37   <dbl> 0.669, 0.384, 0.612, 0.541, 0.460, 0.381, 0.615, 0.192, 0.246, …
#> $ V38   <dbl> 0.6097, 0.1057, 0.6756, 0.8814, 0.3217, 0.4158, 0.4283, 0.3930,…
#> $ V39   <dbl> 0.4943, 0.1840, 0.5375, 0.9857, 0.2828, 0.4054, 0.5479, 0.4288,…
#> $ V40   <dbl> 0.2744, 0.1970, 0.4719, 0.9167, 0.2430, 0.3296, 0.6133, 0.2546,…
#> $ V41   <dbl> 0.0510, 0.1674, 0.4647, 0.6121, 0.1979, 0.2707, 0.5017, 0.1151,…
#> $ V42   <dbl> 0.2834, 0.0583, 0.2587, 0.5006, 0.2444, 0.2650, 0.2377, 0.2196,…
#> $ V43   <dbl> 0.2825, 0.1401, 0.2129, 0.3210, 0.1847, 0.0723, 0.1957, 0.1879,…
#> $ V44   <dbl> 0.4256, 0.1628, 0.2222, 0.3202, 0.0841, 0.1238, 0.1749, 0.1437,…
#> $ V45   <dbl> 0.2641, 0.0621, 0.2111, 0.4295, 0.0692, 0.1192, 0.1304, 0.2146,…
#> $ V46   <dbl> 0.1386, 0.0203, 0.0176, 0.3654, 0.0528, 0.1089, 0.0597, 0.2360,…
#> $ V47   <dbl> 0.1051, 0.0530, 0.1348, 0.2655, 0.0357, 0.0623, 0.1124, 0.1125,…
#> $ V48   <dbl> 0.1343, 0.0742, 0.0744, 0.1576, 0.0085, 0.0494, 0.1047, 0.0254,…
#> $ V49   <dbl> 0.0383, 0.0409, 0.0130, 0.0681, 0.0230, 0.0264, 0.0507, 0.0285,…
#> $ V50   <dbl> 0.0324, 0.0061, 0.0106, 0.0294, 0.0046, 0.0081, 0.0159, 0.0178,…
#> $ V51   <dbl> 0.0232, 0.0125, 0.0033, 0.0241, 0.0156, 0.0104, 0.0195, 0.0052,…
#> $ V52   <dbl> 0.0027, 0.0084, 0.0232, 0.0121, 0.0031, 0.0045, 0.0201, 0.0081,…
#> $ V53   <dbl> 0.0065, 0.0089, 0.0166, 0.0036, 0.0054, 0.0014, 0.0248, 0.0120,…
#> $ V54   <dbl> 0.0159, 0.0048, 0.0095, 0.0150, 0.0105, 0.0038, 0.0131, 0.0045,…
#> $ V55   <dbl> 0.0072, 0.0094, 0.0180, 0.0085, 0.0110, 0.0013, 0.0070, 0.0121,…
#> $ V56   <dbl> 0.0167, 0.0191, 0.0244, 0.0073, 0.0015, 0.0089, 0.0138, 0.0097,…
#> $ V57   <dbl> 0.0180, 0.0140, 0.0316, 0.0050, 0.0072, 0.0057, 0.0092, 0.0085,…
#> $ V58   <dbl> 0.0084, 0.0049, 0.0164, 0.0044, 0.0048, 0.0027, 0.0143, 0.0047,…
#> $ V59   <dbl> 0.0090, 0.0052, 0.0095, 0.0040, 0.0107, 0.0051, 0.0036, 0.0048,…
#> $ V60   <dbl> 0.0032, 0.0044, 0.0078, 0.0117, 0.0094, 0.0062, 0.0103, 0.0053,…
#> $ Class <fct> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, …

tibble::as_tibble(Sonar)
#> # A tibble: 208 x 61
#>       V1     V2     V3     V4     V5     V6    V7    V8     V9   V10    V11
#>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1 0.02   0.0371 0.0428 0.0207 0.0954 0.0986 0.154 0.160 0.311  0.211 0.161 
#> 2 0.0453 0.0523 0.0843 0.0689 0.118  0.258  0.216 0.348 0.334  0.287 0.492 
#> 3 0.0262 0.0582 0.110  0.108  0.0974 0.228  0.243 0.377 0.560  0.619 0.633 
#> 4 0.01   0.0171 0.0623 0.0205 0.0205 0.0368 0.110 0.128 0.0598 0.126 0.0881
#> 5 0.0762 0.0666 0.0481 0.0394 0.059  0.0649 0.121 0.247 0.356  0.446 0.415 
#> 6 0.0286 0.0453 0.0277 0.0174 0.0384 0.099  0.120 0.183 0.210  0.304 0.299 
#> # … with 202 more rows, and 50 more variables: V12 <dbl>, V13 <dbl>, V14 <dbl>,
#> #   V15 <dbl>, V16 <dbl>, V17 <dbl>, V18 <dbl>, V19 <dbl>, V20 <dbl>,
#> #   V21 <dbl>, V22 <dbl>, V23 <dbl>, V24 <dbl>, V25 <dbl>, V26 <dbl>,
#> #   V27 <dbl>, V28 <dbl>, V29 <dbl>, V30 <dbl>, V31 <dbl>, V32 <dbl>,
#> #   V33 <dbl>, V34 <dbl>, V35 <dbl>, V36 <dbl>, V37 <dbl>, V38 <dbl>,
#> #   V39 <dbl>, V40 <dbl>, V41 <dbl>, V42 <dbl>, V43 <dbl>, V44 <dbl>,
#> #   V45 <dbl>, V46 <dbl>, V47 <dbl>, V48 <dbl>, V49 <dbl>, V50 <dbl>,
#> #   V51 <dbl>, V52 <dbl>, V53 <dbl>, V54 <dbl>, V55 <dbl>, V56 <dbl>,
#> #   V57 <dbl>, V58 <dbl>, V59 <dbl>, V60 <dbl>, Class <fct>

# create 80%/20% for training and validation datasets
validationIndex <- createDataPartition(Sonar$Class, p=0.80, list=FALSE)
validation <- Sonar[-validationIndex,]
training   <- Sonar[validationIndex,]

tic()
# train a model and summarize model
set.seed(7)
trainControl <- trainControl(method="repeatedcv", number=10, repeats=3)
fit.rf <- train(Class~., data=training, 
                method = "rf", 
                metric = "Accuracy", 
                trControl = trainControl, 
                ntree = 2000)
toc()
#> 71.779 sec elapsed
print(fit.rf)
#> Random Forest 
#> 
#> 167 samples
#>  60 predictor
#>   2 classes: 'M', 'R' 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (10 fold, repeated 3 times) 
#> Summary of sample sizes: 150, 150, 150, 151, 151, 150, ... 
#> Resampling results across tuning parameters:
#> 
#>   mtry  Accuracy  Kappa
#>    2    0.845     0.682
#>   31    0.828     0.651
#>   60    0.808     0.611
#> 
#> Accuracy was used to select the optimal model using the largest value.
#> The final value used for the model was mtry = 2.
print(fit.rf$finalModel)
#> 
#> Call:
#>  randomForest(x = x, y = y, ntree = 2000, mtry = param$mtry) 
#>                Type of random forest: classification
#>                      Number of trees: 2000
#> No. of variables tried at each split: 2
#> 
#>         OOB estimate of  error rate: 14.4%
#> Confusion matrix:
#>    M  R class.error
#> M 84  5      0.0562
#> R 19 59      0.2436

Accuracy: 85.26% at mtry=2

22.4 Apply tuning parameters for final model

# create standalone model using all training data
set.seed(7)
finalModel <- randomForest(Class~., training, mtry=2, ntree=2000)

# make a predictions on "new data" using the final model
finalPredictions <- predict(finalModel, validation[,1:60])
confusionMatrix(finalPredictions, validation$Class)
#> Confusion Matrix and Statistics
#> 
#>           Reference
#> Prediction  M  R
#>          M 20  4
#>          R  2 15
#>                                         
#>                Accuracy : 0.854         
#>                  95% CI : (0.708, 0.944)
#>     No Information Rate : 0.537         
#>     P-Value [Acc > NIR] : 1.88e-05      
#>                                         
#>                   Kappa : 0.704         
#>                                         
#>  Mcnemar's Test P-Value : 0.683         
#>                                         
#>             Sensitivity : 0.909         
#>             Specificity : 0.789         
#>          Pos Pred Value : 0.833         
#>          Neg Pred Value : 0.882         
#>              Prevalence : 0.537         
#>          Detection Rate : 0.488         
#>    Detection Prevalence : 0.585         
#>       Balanced Accuracy : 0.849         
#>                                         
#>        'Positive' Class : M             
#>

Accuracy: 82.93%

22.5 Save model

# save the model to disk
saveRDS(finalModel, file.path(model_out_dir, "sonar-finalModel.rds"))

22.6 Use the saved model

# load the model
superModel <- readRDS(file.path(model_out_dir, "sonar-finalModel.rds"))
print(superModel)
#> 
#> Call:
#>  randomForest(formula = Class ~ ., data = training, mtry = 2,      ntree = 2000) 
#>                Type of random forest: classification
#>                      Number of trees: 2000
#> No. of variables tried at each split: 2
#> 
#>         OOB estimate of  error rate: 16.2%
#> Confusion matrix:
#>    M  R class.error
#> M 81  8      0.0899
#> R 19 59      0.2436

22.7 Make prediction with new data

# make a predictions on "new data" using the final model
finalPredictions <- predict(superModel, validation[,1:60])
confusionMatrix(finalPredictions, validation$Class)
#> Confusion Matrix and Statistics
#> 
#>           Reference
#> Prediction  M  R
#>          M 20  4
#>          R  2 15
#>                                         
#>                Accuracy : 0.854         
#>                  95% CI : (0.708, 0.944)
#>     No Information Rate : 0.537         
#>     P-Value [Acc > NIR] : 1.88e-05      
#>                                         
#>                   Kappa : 0.704         
#>                                         
#>  Mcnemar's Test P-Value : 0.683         
#>                                         
#>             Sensitivity : 0.909         
#>             Specificity : 0.789         
#>          Pos Pred Value : 0.833         
#>          Neg Pred Value : 0.882         
#>              Prevalence : 0.537         
#>          Detection Rate : 0.488         
#>    Detection Prevalence : 0.585         
#>       Balanced Accuracy : 0.849         
#>                                         
#>        'Positive' Class : M             
#>

21 Linear and Non-Linear Algorithms for Classification

23 Predicting the type of glass