How to choose the features for a neural network?

How to choose the features for a neural network?

The beauty of neural networks is that little feature engineering and preprocessing is required — features are instead learned by intermediate layers. Whenever possible, prefer learning features to engineering them. If you are using scikit-learn, there is a good function available called model.feature_importances_.

What should I do when my neural network doesn’t learn?

Residual connections can improve deep feed-forward networks. To achieve state of the art, or even merely good, results, you have to have to have set up all of the parts configured to work well together. Setting up a neural network configuration that actually learns is a lot like picking a lock: all of the pieces have to be lined up just right.

What happens when there are too many neurons in a neural network?

Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. Too many neurons can cause over-fitting because the network will “memorize” the training data.

Why are there so many bugs in neural networks?

The network initialization is often overlooked as a source of neural network bugs. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior.

How can neural networks be used to predict the stock market?

The goal of this article is to provide a practical introductory guide to neural networks for forecasting financial time series data using Azure Deep Learning Virtual Machine. A multiple step approach to design a neural network forecasting model will be explained, including an application of stock market predictions with LSTM in Python.

How is artificial neural network used in weather forecasting?

In work Weather Forecasting Model using Artificial Neural Network , authors a model that can reduce this processing cost by working on raw data.

Which is a good sign of a good feature?

A strong linear correlation between the new feature and the predicted variable is an good sign that a new feature will be valuable, but the absence of a high correlation is not necessary a sign of a poor feature, because neural networks are not restricted to linear combinations of variables.

How to use caret to tune a neural network?

I’d like to use the caret package in R to tune a neural network classification model on my data. In order to do this, I first have to reduce the size of the predictor-set with feature selection to make it computationally feasible, correct? Lets assume that is correct. Then the question becomes “which feature selection method?”.

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.

Is the RFE function optimal for neural networks?

If I were to do RF-RFE using the rfe function in caret, for example, I would get an optimal feature set specific to random forests and not necessarily optimal for neural networks. Any advice?