Is neural network good for text classification?
Word Embeddings + CNN = Text Classification Yoav Goldberg, in his primer on deep learning for natural language processing, comments that neural networks in general offer better performance than classical linear classifiers, especially when used with pre-trained word embeddings.
Which type of Bayes classifier is best suited for document classification problem?
Multinomial Naive Bayes: This is mostly used for document classification problem, i.e whether a document belongs to the category of sports, politics, technology etc. The features/predictors used by the classifier are the frequency of the words present in the document.
What is Bayesian neural network used for?
Bayesian neural nets are useful for solving problems in domains where data is scarce, as a way to prevent overfitting. Example applications are molecular biology and medical diagnosis (areas where data often come from costly and difficult experimental work).
How is a Bayesian neural network used in classification?
But, a Bayesian neural network will have a probability distribution attached to each layer as shown below. For a classification problem, you perform multiple forward passes each time with new samples of weights and biases.
Why does a neural network underperform in classification?
The neural network will work really well with training data but underperforms when it is fed unseen data. This makes the network blind to the uncertainties in the training data and tends to be overly confident in its wrong predictions. Consider an example where you are trying to classify a car and a bike.
Why do we need a deep neural network?
Deep neural networks learn to form relationships with the given data without having prior exposure to the dataset. They perform very well on non-linear data and hence require large amounts of data for training. Although more information is better for the network, it leads to problems like overfitting .
How is the softmax function used in classification?
Consider an example where you are trying to classify a car and a bike. If an image of a truck is shown to the network, it ideally should not predict anything. But, because of the softmax function, it assigns a high probability to one of the classes and the network wrongly, though confidently predicts it to be a car.