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Do neural networks need feature selection?
Neural networks are themselves often used for feature selection. This is the paradigm leading to deep learning. In that case it is unlikely you’d want to do any feature selection (except maybe whitening of the data).
Do we need feature selection for deep learning?
So, the conclusion is that Deep Learning Networks do not need a previos feature selection step. Deep learning in its layers performs feature selection as well. Deep learning algorithm learn the features from the data instead of handcrafted feature extraction. It does not use PCA.
Does deep learning require more feature engineering work?
While deep learning reduces the human effort of feature engineering, as this is automatically done by the machine, it also increases the difficulty for humans to understand and interpret the model. In fact, model interpretability is one of deep learning’s biggest challenges.
Why do we need feature selection in neural networks?
Feature engineering lets the practitioner directly transform knowledge about the problem into a fixed-length vector amenable to feed-forward networks. Feature selection can solve the problem of including so many irrelevant features that any signal is lost, as well as dramatically reducing the number of parameters to the model.
Why do we need feature selection in deep learning?
Feature selection can solve the problem of including so many irrelevant features that any signal is lost, as well as dramatically reducing the number of parameters to the model. The key words here are priors and scale.
Why do we need to use feature selection methods?
Models have increasing risk of overfitting with increasing number of features. Feature Selection methods helps with these problems by reducing the dimensions without much loss of the total information. It also helps to make sense of the features and its importance.
Can a deep neural network approximate any function?
A deep neural network can indeed approximate any function in theory, but the dimension of the parameter space can be really large, like in the millions. So, actually finding a good neural network is really difficult.