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I created a neural network model for a classification task based on 14 variables, 1 hidden layer of size 8. Outputs give 3 possible classes.

The weights I got from input (left) and output (right) layers:

enter image description here

I struggle to interpret the relevance of weights attributed to the variables (v1, v2, ...) among the neurons (n1, n2,...).

The variables v4 and v9 are initially guessed as most relevant for classifying. However, they have very distinct weights among the neurons (17 or 10 to -12).

Are negative weights as important as positive weights?

In the output layer, v7 always has negative weights, which reduces the importance of the highest weight of v4 on n7.

I also did repeated runs of independent training sessions to check the behavior of the model, and then I summed the total positive and negative weights of each variable from the input layer each time. The results showed that v4 and v7 are apparently always top ranked (see below). But considering the output layer, and the distribution of negative weights, I realized that v4 and v7 are perhaps not the best variables for classification. enter image description here

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  • $\begingroup$ Good question. I think the input matrix can be viewed as an embedding of your variables. It is an 8-dimensional Euclidean space in which the variables that are related in terms of your prediction task are clustered together. The rows of the matrix are just the coordinates of each variable in your embedding space. When initializing the network, the variables are randomly scattered in the space, and then as training goes by backprop clusters the related variables together and makes the unrelated ones more distant. Check out: ronxin.github.io/wevi $\endgroup$ – Antoine Jun 16 '17 at 11:05
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    $\begingroup$ Possible duplicate of Can't deep learning models now be said to be interpretable? Are nodes features? $\endgroup$ – Reinstate Monica May 29 '18 at 17:20
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The interpretation of weights is generally a challenging task.

Regarding your specific concern:

  • The negative weights are as important as positives. The fact that a weight is negative means that most of the data records have "voted" for having it negative to have a better fit. If you started the learning from values close to zero, then it was the data who made the weight negative by backpropagation.
  • Considering your v4 and v7 as favorites, I would recommend you to repeat the learning process with these variables only and compare the quality (accuracy/recall/precision) of the new model with model based on all variables.
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