The functions contained in the package work with binary and multiclassification methods.
The Random Forest shows that it has been trained on greater than >2 classes so this moves from a binary model to a multi-classification model. This is not a lesson on machine learning, however we now know how well the model performs on the training set, we need to validate this with a confusion matrix. #> The final value used for the model was mtry = 3.
Draw_confusion_matrix <- function(cm) Ĭonf_matrix <- function(df.true, df.pred, title = "", true.lab ="True Class", pred.lab ="Predicted Class", l = 'red', low.Rf_model Random Forest #> #> 150 samples #> 4 predictor #> 3 classes: 'setosa', 'versicolor', 'virginica' #> #> No pre-processing #> Resampling: Bootstrapped (25 reps) #> Summary of sample sizes: 150, 150, 150, 150, 150, 150.
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