Negative log-likelihoodIt is a loss function used to solve classification problems. It is a natural logarithm form of the likelihood function, which can be used to measure the similarity between two probability distributions. The negative sign is used to make the maximum likelihood value correspond to the minimum loss. It is a common function form in maximum likelihood estimation and related fields.

In machine learning, it is customary to use optimization algorithms to find the minimum value, so negative log-likelihood is used. This is a common loss function in classification problems and can be extended to multi-classification problems.

Negative Log-Likelihood and Likelihood Estimation

There is a method called "maximum likelihood estimation" in parameter estimation. Because the estimation function involved is often an exponential family, taking the logarithm does not affect its monotonicity, but it will make the calculation process simpler.

Depending on the model involved, the logarithmic function may be different, but they all come from the density function of the dependent variable and involve assumptions about the distribution of random interference terms.

Parent term: Log-likelihood function