The `score` function in `sklearn.neighbors.KernelDensity` is used to compute the log-likelihood of a set of samples under the kernel density model. It returns the logarithm of the estimated probability density of the samples. This method is helpful to assess the goodness of fit of the density model to the data by comparing the scores of different models or by comparing the score of a model to a reference value. A higher score indicates a better fit of the model to the data. Additionally, the `score` function can be used for model selection or to compare the performance of different kernel density estimators.
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