Getting Started

Using autoModel

We begin by loading the mlbench package and some example data Sonar which is commonly used to binary classification problems. In this example, we will attempt to distinguish Mines (M) from Rocks (R) using binary classification with an initial dataset of where N=208 and P=60.

As a general rule, when using autoCaret::autoModel defaults, datasets less than 100 mb should yield optimal performance and likely avoid extremely long run times & high memory requirements.

library(mlbench)
library(autoCaret)

# Load the data into Memory from the mlbench package
data("Sonar")

# Summarize Sonar's Size
dim(Sonar)
#> [1] 208  61

# Check out Variable Names
names(Sonar)
#>  [1] "V1"    "V2"    "V3"    "V4"    "V5"    "V6"    "V7"    "V8"   
#>  [9] "V9"    "V10"   "V11"   "V12"   "V13"   "V14"   "V15"   "V16"  
#> [17] "V17"   "V18"   "V19"   "V20"   "V21"   "V22"   "V23"   "V24"  
#> [25] "V25"   "V26"   "V27"   "V28"   "V29"   "V30"   "V31"   "V32"  
#> [33] "V33"   "V34"   "V35"   "V36"   "V37"   "V38"   "V39"   "V40"  
#> [41] "V41"   "V42"   "V43"   "V44"   "V45"   "V46"   "V47"   "V48"  
#> [49] "V49"   "V50"   "V51"   "V52"   "V53"   "V54"   "V55"   "V56"  
#> [57] "V57"   "V58"   "V59"   "V60"   "Class"

Having both the data loaded and having inspected it, we can now make use of the autoCaret::autoModel() function.

In this example, we’d like to try and distinguish Rocks (R) from Mines (R), so we will attempt to predict the Class variable in the Sonar dataframe.

Using it’s defaults, autoModel has 2 arguments we need to specify: df and y.

df is the Dataframe that we’d like to use build a binary classification model, while y is our classification target or response variable. We can use a non-exported package function, autoCaret:::checkBinaryTrait to determine if our y variable is indeed binary. The autoModel functionality will perform this for us as well.

# Manually check that our intended y paramter is indeed binary
autoCaret:::checkBinaryTrait(Sonar$Class)
#> [1] TRUE

# Generate an autoCaret object using the autoModel function
mod <- autoCaret::autoModel(df = Sonar, y = Class, progressBar = FALSE)

Exploring an autoCaret object

In the example above, the returned object, mod, is an autoCaret object containing 16 objects. To confirm, we can run the below two commmands:

# Check class of autoCaret object
class(mod)
#> [1] "autoCaret"

# High level 
nrow(summary.default(mod))
#> [1] 19

Running the summary function on our model output displays a wealth of information about the contents of the object as well as the procedural steps taken during modeling. In our example, we observe:

• that our initial dataset of 208 observation was split into a training and test set containing 167 and 41 observations
• Modeling took .64 minutes and entailed resampling our dataset 10 times
• We used the four default models to create an ensemble.
• Using the ensemble model that was generated to predict on the test set yield predictions with 92% accuracy.

# Use the summary generic to store a summary of autoCaret object
overview <- summary(mod)
#> The input dataset had: 208 observations and 60 predictors 
#> ---------------------
#> Prior to model training, the input dataset was split into a training & test set 
#> The training set has:  167  observations 
#> The test set has:      41  observations 
#> ---------------------
#> Overall modeling took: 0.62 minutes 
#> During that time the training data was boostrap resampled 10 times 
#> 
#> The following classification models were used to create an ensemble: 
#> - glm
#> - rpart
#> - rf
#> - xgbLinear
#> To learn more about ensemble learing in the context of machine learning, please visit: https://en.wikipedia.org/wiki/Ensemble_learning 
#> 
#> In the ensemble, the top 5 variables in order from highest to lowest level of relative importance, were: 
#> -  V11
#> -  V12
#> -  V9
#> -  V10
#> -  V36
#> ---------------------
#> When the ensemble model was used to predict on the held out test set of 41 observations it performed as follows: 
#> 
#> Overall Accuracy: 92.68
#> 
#> A confusion matrix demonstrating accuracy is as follows: 
#>           Reference
#> Prediction  R  M
#>          R 18  2
#>          M  1 20
#> 
#> Precision: 90
#> Recall: 94.74
#> ---------------------
#> To learn more about Precision & Recall in the context of information retriveal, please visit: https://en.wikipedia.org/wiki/Precision_and_recall

We can also access each of the object variables included in the above displayed summary output via the object itself.

# Print the overview to the console
overview
#> $input_row_count
#> [1] 208
#> 
#> $input_col_count
#> [1] 61
#> 
#> $train_row_count
#> [1] 167
#> 
#> $test_row_count
#> [1] 41
#> 
#> $modeling_time
#> [1] 0.62
#> 
#> $number_resamples
#> [1] 10
#> 
#> $method_list
#> [1] "glm"       "rpart"     "rf"        "xgbLinear"
#> 
#> $confusionMatrix
#>           Reference
#> Prediction  R  M
#>          R 18  2
#>          M  1 20
#> 
#> $accuracy
#> [1] 92.68
#> 
#> $sensitivity
#> [1] 94.74
#> 
#> $specificity
#> [1] 90.91
#> 
#> $precision
#> [1] 90
#> 
#> $recall
#> [1] 94.74
#> 
#> $best_model_results
#>   model_name       ROC      Sens      Spec      ROCSD     SensSD
#> 1   ensemble 0.9337900 0.8095031 0.8953181 0.01498452 0.01089121
#> 2         rf 0.9052501 0.6901687 0.8829980 0.03551813 0.10603065
#> 3  xgbLinear 0.8702916 0.7168320 0.8185311 0.04529174 0.10874655
#> 4      rpart 0.7019527 0.6562942 0.7122567 0.08602055 0.15284623
#> 5        glm 0.6451431 0.5910052 0.6543618 0.08914517 0.08671003
#>       SpecSD
#> 1 0.04371527
#> 2 0.06784535
#> 3 0.08614794
#> 4 0.07226794
#> 5 0.10772096
#> 
#> attr(,"class")
#> [1] "summary.autoCaret"

Predicting new data

So now that we have a sense of how successful our auto modeling approach was, we’d likely want to use the model, mod, we built previously to make predictions on new data we receive.

Because this is an illustrative example, we’lll take a shortcut by just resampling the same data that we used to train on. The main point here is that you can simply pass your autoCaret model object, mod, into the predict() function along with new observations to generate predictions.

#For the sake of example, simulate new data by resampling our original data frame
new <- Sonar[sample(1:nrow(Sonar), 50, replace=TRUE),]

#Make predicitons 
preds <- predict(mod, new)

#Print Predictions 
preds
#>  [1] M R M R R R R M R M R M M R M R R M M R M M M M R M M M R R M R R M M
#> [36] R M M M R R M M M R M M M M R
#> Levels: R M

How well did we do? Well a confusion matrix from the caret package can tell us!

• We only mispredicted one example, for overall accuracy of .98
• Note: we wouldn’t expect this level of accuracy using real data given we resampled from our original training set.

## How well did we do?
caret::confusionMatrix(data = preds, reference = new$Class)
#> Confusion Matrix and Statistics
#> 
#>           Reference
#> Prediction  M  R
#>          M 29  0
#>          R  1 20
#>                                           
#>                Accuracy : 0.98            
#>                  95% CI : (0.8935, 0.9995)
#>     No Information Rate : 0.6             
#>     P-Value [Acc > NIR] : 2.775e-10       
#>                                           
#>                   Kappa : 0.9587          
#>  Mcnemar's Test P-Value : 1               
#>                                           
#>             Sensitivity : 0.9667          
#>             Specificity : 1.0000          
#>          Pos Pred Value : 1.0000          
#>          Neg Pred Value : 0.9524          
#>              Prevalence : 0.6000          
#>          Detection Rate : 0.5800          
#>    Detection Prevalence : 0.5800          
#>       Balanced Accuracy : 0.9833          
#>                                           
#>        'Positive' Class : M               
#> 

Using the autoCaret add-in

As an alternative option to the CLI, we provide an intuitive GUI in the form an RStudio Addin.

The add-in may be accessed in two ways:

• Via the Addin menu following loading autoCaret via library(autoCaret) in a recent release of Rstudio
• By running the following non-exported function autoCaret:::autoCaretUI().

#For the sake of example, simulate new data by resampling our original data frame
new <- Sonar[sample(1:nrow(Sonar), 50, replace=TRUE),]