R Deep Learning Projects: Master the techniques to design and develop neural network models in R by Yuxi (Hayden) Liu & Pablo Maldonado

Author:Yuxi (Hayden) Liu & Pablo Maldonado [Liu, Yuxi (Hayden)]
Language: eng
Format: epub
Tags: COM037000 - COMPUTERS / Machine Theory, COM018000 - COMPUTERS / Data Processing, COM062000 - COMPUTERS / Data Modeling and Design
Publisher: Packt Publishing
Published: 2018-02-21T23:00:00+00:00

Distribution of reconstruction error in the test set

From the plot, we see that we can set up the threshold value at 15, that is, observations with a reconstruction error above 15 would be marked as outliers:

threshold <- 15

y_preds <- sapply(error, function(x) ifelse(x>threshold,"outlier","normal")})

Once this is done, we can calculate the confusion matrix. This is a useful way of visualizing what the model is doing:

# Confusion matrix

table(y_preds,y_test)

This gives us the following:

y_test

y_preds normal outlier

normal 5707 496

outlier 3265 532

So clearly we could do better. Perhaps the vertical stroke shared by digits 1 and 7 contributes to the huge error rate. We caught, however, a bit over 50% of the outlier cases with this simple architecture. One way to improve this would be to add more hidden layers. We will use this trick later in this chapter.

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