# arviz.loo¶

arviz.loo(data, pointwise=False, reff=None, scale='deviance')[source]

Pareto-smoothed importance sampling leave-one-out cross-validation.

Calculates leave-one-out (LOO) cross-validation for out of sample predictive model fit, following Vehtari et al. (2017). Cross-validation is computed using Pareto-smoothed importance sampling (PSIS).

Parameters: data : result of MCMC run pointwise: bool, optional if True the pointwise predictive accuracy will be returned. Defaults to False reff : float, optional Relative MCMC efficiency, effective_n / n i.e. number of effective samples divided by the number of actual samples. Computed from trace by default. scale : str Output scale for loo. Available options are: deviance : (default) -2 * (log-score) log : 1 * log-score (after Vehtari et al. (2017)) negative_log : -1 * (log-score) pandas.Series with the following columns: loo: approximated Leave-one-out cross-validation loo_se: standard error of loo p_loo: effective number of parameters shape_warn: 1 if the estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples loo_i: array of pointwise predictive accuracy, only if pointwise True pareto_k: array of Pareto shape values, only if pointwise True loo_scale: scale of the loo results