MATLAB and Simulink resources for Arduino, LEGO, and Raspberry Pi

Learn moreOpportunities for recent engineering grads.

Apply Today
Asked by Bogdan on 11 Dec 2012

Hello people :)

Before I begin, I want to say that I am a beginner in the Neural Network field so, if the question seems stupid... please give me a correct answer :)

Yesterday I began to experiment with the Neural Network tools, more precisely with **nnstart** and **nftool**. I started with the famous house pricing example. I did everything the tutorial told me to do and I got an answer consisting in a matrix with one row and 506 columns, stored in the **output** variable.

The first three elements are as follows:

- 24.6073
- 22.6764
- 34.6302

And so on. So far so good. But here are my questions:

- How to interpret those results? What should I understand from them? That the median price for neighborhood 1 should be or will be 24.6073? Or how?
- Let's say that I have a 507th set of data. How can I find the answer for
**that single**set of data, based on the data that the NN already has, without entering it into the input variables, re-training the network, etc.?

Thank you in advance.

Answer by Greg Heath on 12 Dec 2012

Edited by Greg Heath on 12 Dec 2012

Accepted answer

Q1. How to interpret those results? What should I understand from them? That the median price for neighborhood 1 should be or will be 24.6073?

A1. For a house with those 13 input parameters, the predicted median house price is $24,607.30

Q2.Let's say that I have a 507th set of data. How can I find the answer for that single set of data, based on the data that the NN already has, without entering it into the input variables, re-training the network, etc.?

A2. The purpose of training the model is to estimate a median price given the 13 input parameters. Since the model is trained, to estimate the price of other houses not in the original set of 506, just use

priceestimate = net(newinput)

Otherwise design another model.

Hope this helps.

Thank you for formally accepting this answer.

Greg

## 0 Comments