How it works...
A neural network increases its efficiency when it improves its generalization power. A neural network should not just memorize a certain decision-making process in favor of a particular label. If it does, our outcomes will be biased and wrong. So, it is good to have a dataset where the labels are uniformly distributed. If they're not uniformly distributed, then we might have to adjust a few things while calculating the error rate. For this purpose, we introduced a weightsArray in step 1 and added to OutputLayer in step 2.
For weightsArray = {0.35, 0.65}, the network gives more priority to the outcomes of 1 (customer unhappy). As we discussed earlier in this chapter, the Exited column represents the label. If we observe the ...
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