How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

Related MCQs

• Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?
•  Given above is a description of a neural network. When does a neural network model become a deep learning model?
• Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node
• True or False: Dropout is computationally expensive technique w.r.t. bagging
• Neural Networks are complex ______________ with many parameters.
• Which statement is true about neural network and linear regression models?
• Which of the following is an application of NN (Neural Network)?
• Which one of the following is not a major strength of the neural network approach?
• It is possible to design a Linear regression algorithm using a neural network?
• It is possible to design a Linear regression algorithm using a neural network?