wehub-resource-sync
16 lines
789 B
Markdown
16 lines
789 B
Markdown
# What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?
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The softmax function is simply a generalization of the logistic function that allows us to compute meaningful class-probabilities in multi-class settings (multinomial logistic regression). In softmax, we compute the probability that a particular sample (with net input z) belongs to the *i*th class using a normalization term in the denominator that is the sum of all *M* linear functions:
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In contrast, the logistic function:
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And for completeness, we define the net input as
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where the weight coefficients of your model are stored as vector "w" and "x" is the feature vector of your sample.
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