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Each neuron in the input layer represents a predictor variable. In categorical variables, ''N-1'' neurons are used when there are ''N'' number of categories. It standardizes the range of the values by subtracting the median and dividing by the [[interquartile range]]. Then the input neurons feed the values to each of the neurons in the hidden layer.
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This layer contains one neuron for each case in the training data set. It stores the values of the predictor variables for the case along with the target value. A hidden neuron computes the Euclidean distance of the test case from the neuron’s center point and then applies the [[radial basis function]] kernel function using the sigma values.
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