Log-likelihood function

Created
Created
2023 Mar 23 2:13
Creator
Creator
Seonglae ChoSeonglae Cho
Editor
Edited
Edited
2024 Oct 17 9:19
Refs
Refs
Convexity

Logistic log loss is convex

  • Continuous
  • Differentiable

Becuz log also monotonically increasing argmax easy

should maximize likelihood function, so negative value when it is logged
is log likelihood and is negative log likelihood
it measures how well the parameters fit the observed data. The notation used to represent the likelihood function is L(θ), where θ represents the parameters of the model, and X and y represent the data. The likelihood function is defined as the conditional probability of the observed data given the values of the parameters of the model: .
 
 
j function usually means negative log likelihood
 
 
 
 
 
 
 

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