How to interpret the end result of Coffee Machine Neural Network

In the Coffee Machine Neural network, we get the following output in the end
decisions =

How do I interpret the meaning of this result? Is my coffee roasting good or not? If good, then does that mean that the temperature and time I’ve used in roasting it i.e. my input is correct for the purpose?

Hi @Mohd_Farhan_Hassan ,

After the model is trained, it is then ready to be used for make predictions. In this case, on two examples. The prediction function returns the probability for each of the example. This is checked against the threshold of 0.5; probability >= 0.5 is good , ie[1], otherwise bad, ie[0]. This probability can be referenced back to the input, which can be seen in the my_predict() where p[i,0] is from X[i]. If you print out the content of X[i] and p[i,0] you will be able to see which temperature and duration produces a good roast.