R Programming By Example

Computing predictive accuracy and confusion matrices

Now that we have our trained model, we can see its results and ask it to compute some predictive accuracy metrics. We start by simply printing the object we get back from the train() function. As can be seen, we have some useful metadata, but what we are concerned with right now is the predictive accuracy, shown in the Accuracy column. From the five values we told the function to use as testing scenarios, the best model was reached when we used 356 out of the 2,007 available features (tokens). In that case, our predictive accuracy was 65.36%.

If we take into account the fact that the proportions in our data were around 63% of cases with multiple purchases, we have made an improvement. This ...