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# "Neural Net interpret result"

Hi,

im working with neural nets. The problem is, i dont understand how to interpret the result from rapid miner???

Im no mathematican. How should i multiplicate the input value with the weights to get the predicted value? Β I dont know what bias and threshold measns.

I hope you can give me hint.

im working with neural nets. The problem is, i dont understand how to interpret the result from rapid miner???

Im no mathematican. How should i multiplicate the input value with the weights to get the predicted value? Β I dont know what bias and threshold measns.

I hope you can give me hint.

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0

## Answers

3,120RM Data Scientistwhy do you want to understand this result? For me their is no reason to recalculate it. Just use the model.

If you really want to understand it, you will have to understand the math of neural nets.

Cheers,

Martin

Dortmund, Germany

4Contributor II want to predict the turnover of a grocery strore. I have data (turnover and influences on the turnover) of 145 stores.

If i want to establish a new store how can i use the result from rapid miner to predict the turnover of the new store?

Im sure it is possible with rapid miner. Can you help me?

How can i use the model? Can rapid miner show me the equation?

I understand how to recalculate but get a different result.

Greetings

Immo001

329UnicornIt might be useful to provide us more information: what data are you using, which process, what is the result, which conclusions did you take based on the results. How does your result differ from what RM produces. Comparing requires exactly the same parameters, etc.

Good luck!

Sven

4Contributor II use for recalculating and understanding 27 datasets (row). Two attributes, one label (column)

My process:

"Data" --> "x-validation"

In the "x-validation" is the "neural net" on the left side (training), "apply model" and "performance" is on the right side (testing).

In the end of x-validation is again "apply model" connected with multiply to the data.

Rapid miner shows in the input layer attribute 1, attribute 2 and a threshold node.

In the hidden layer are three noddes and a threshold node.

There is only one output node.

Descripted Result:

Node 1

1 att. -1.763

2 att. -1.144

Bias

Node 2

1 att. -1.776

2 att. -1.103

Bias -1.178

Node 3

1 att. -1.937

2 att. -1.937

Bias -0.996

Output

1 Node -1.389

2 Node -1.376

3 Node -1.495

Threshold 0.112

Rapid miner gives me also a predicted value for every row.

Now I want to use the first row of my dataset (att. 1 and 2) to recalculate the predicted result.

The Idea is, if i know how to recalculate I can calculate new data. Or use the formula in a Excel sheet. In my exapmle it is the turnover of a neu grocery store.

My calculations (1att. 0.532, 2. att 0.089, the predicted result is 0.341)

Node 1

0.532 * (-1.763) + 0.089 * (-1.144) + (-1.173) = -2,212

after the sigmoid transformation I get 0,098

Node 2

0.532 * (-1.776) + 0.089 * (-1.103) + (-1.178) = -2,220

after the sigmoid transformation I get 0,097

Node 3

0.532 * (-1.937) + 0.089 * (-1.254) + -0.996 = -2,137

after the sigmoid transformation I get 0,105

Then I do the linear regression. Im not sure if it is right. I dont know how to use the threshold value.

0,098 Β (-1.389)

0,097 Β (-1.376)

0,105 Β (-1.495)

I get a correlation coefficient of 0,999

but I expect 0.341 ????

Do you understand my problem?

I hope somebody can help me. If you need detail please ask me.

How can rapid miner predict the turnover of a new grocery store (att. 1+ 2, but without a label/turnover)?

I appreciate your help.

Greeting Immo001

2Contributor II made an prediction task, using input with many numbers and labels.

But when the result show the neural net with nodes and theirs weights, i dont know how to make an regresstion calculator by excel.Β

I see on the internet that when we use this for classifier rapidminer use active function (sigmoid) and when the predict result is regression, rapidminer will use linear .

I also think linear is this:Β Y (output) = W*x

w is the matrix of weights and x is the matrix of input.