Can neural networks use categorical variables?

Can neural networks use categorical variables?

A categorical variable is a variable whose values take on the value of labels. Machine learning algorithms and deep learning neural networks require that input and output variables are numbers. This means that categorical data must be encoded to numbers before we can use it to fit and evaluate a model.

Can neural network work with continuous variable?

A neural net can , at least theoretically, approximate any continuous function. It is called the Universal approximation theorem. Of course it might still be hard to learn but in practice it generally works quite well even if you don’t find the optimal solution.

How can a neural network predict a continuous variable?

3 Answers

1. Ensure that your output vector for training and test data is exactly what you need, continuous for each element of output vector.
2. Use what you said and familiar for the layers before the last layer.
3. For the last layer use a dense layer with n, number of outputs, outputs each having linear activation, y = x.

How are inputs represented in a neural network?

You could think of neural network inputs as representing a kind of “intensity”: i.e., larger values of the input variable represent greater intensity of that input variable.

Can a neural network learn a categorical variable?

After all, assuming the network has only one input, a given hidden node of the network is going to learn some function f ( w x + b). where f is the transfer function (e.g. the sigmoid) and x the input variable. This setup does not make sense for categorical variables.

What’s the difference between binary and discrete neural networks?

The only difference is, in first architecture , you have increase the number of input so you more number of weight in first hidden layer’s node to model the impulse function but for the second architecture you need more number of node in hidden layer compared to first architecture to get same performance.

Which is better binary or discrete input nodes?

Of course, binary nodes for every discrete input value mean more input layer nodes (and thus more hidden layer nodes), but would it really produce a better output classification than having the same values in one node, with a well-fitting threshold function in the hidden layer?