Evaluating the model and predicting classes

In this section, we will evaluate the model based on our training and test data. We will obtain accuracy by correctly classifying each author using a confusion matrix for the training and test data to gain further insights. We will also use bar plots to visualize the accuracy of identifying each author.

Model evaluation with training data

First, we will evaluate the model's performance using training data. Then, we will use the model to predict the class representing each of the 50 authors. The code for evaluating the model is as follows:

# Loss and accuracy
model %>% evaluate(trainx, trainy)
$loss
[1] 1.45669
$acc
[1] 0.5346288

Here, we can see that, by using the training data...