COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Decrease bias, increase variance
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Increase bias, increase variance
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Increase bias, decrease variance
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No change
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Detailed explanation-1: -Adding more hidden units should decrease bias and increase variance. In general, more complicated models will result in lower bias but larger variance, and adding more hidden units certainly makes the model more complex.
Detailed explanation-2: -An inordinately large number of neurons in the hidden layers can increase the time it takes to train the network. The amount of training time can increase to the point that it is impossible to adequately train the neural network.
Detailed explanation-3: -Increasing the number of layers or number of neuron in a layer leads to an increase in the learning units that extract information from the previous activation to forward it to the next layer. Thereby increasing the performance of the model and reducing bias.
Detailed explanation-4: -If you increase the number of hidden layers in a Multi Layer Perceptron, the classification error of test data always decreases.
Detailed explanation-5: -1) Increasing the number of hidden layers might improve the accuracy or might not, it really depends on the complexity of the problem that you are trying to solve.