# Semiparametric pairwise model outperforms other models.

**A)** Out-of-sample log-likelihood improvement relative to the pairwise model per sample per neuron averaged over subnetworks. Error bars denote variation over subnetworks (1 SD, no errorbars for *N* = 160 since there is only one subpopulation of that size in the entire dataset). The error in likelihood estimation is much smaller than the displayed error bars. **B)** The same as in A) but for single populations from two different experiments–one in which the population is stimulated with a random checkerboard stimulus, and the other where the population responds to a full-field flickering. **C)** The test set error rate averaged over neurons for predicting the response of a neuron from the activities of other neurons in 5 different subpopulations of 100 neurons. **D)** Average (across neurons) error rate decrease achieved by using a semiparametric pairwise model instead of a K-pairwise model for subpopulations of various sizes. Error bars denote 1 SD variation over subnetworks.