This paper compares traditional machine learning models, i.e. Support Vector\nMachine, k-Nearest Neighbors, Decision Tree and Random Forest, with\nFeedforward Neural Network and Long Short-Term Memory. We observe\nthat the two neural networks achieve higher accuracies than traditional models.\nThis paper also tries to figure out whether dropout can improve accuracy\nof neural networks. We observe that for Feedforward Neural Network, applying\ndropout can lead to better performances in certain cases but worse\nperformances in others. The influence of dropout on LSTM models is small.\nTherefore, using dropout does not guarantee higher accuracy.
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