4.7.6. statsmodels.tools.eval_measures¶
some measures for evaluation of prediction, tests and model selection
Created on Tue Nov 08 15:23:20 2011
Author: Josef Perktold License: BSD-3
4.7.6.1. Functions¶
aic(llf, nobs, df_modelwc) |
Akaike information criterion |
aic_sigma(sigma2, nobs, df_modelwc[, islog]) |
Akaike information criterion |
aicc(llf, nobs, df_modelwc) |
Akaike information criterion (AIC) with small sample correction |
aicc_sigma(sigma2, nobs, df_modelwc[, islog]) |
Akaike information criterion (AIC) with small sample correction |
bias(x1, x2[, axis]) |
bias, mean error |
bic(llf, nobs, df_modelwc) |
Bayesian information criterion (BIC) or Schwarz criterion |
bic_sigma(sigma2, nobs, df_modelwc[, islog]) |
Bayesian information criterion (BIC) or Schwarz criterion |
hqic(llf, nobs, df_modelwc) |
Hannan-Quinn information criterion (HQC) |
hqic_sigma(sigma2, nobs, df_modelwc[, islog]) |
Hannan-Quinn information criterion (HQC) |
iqr(x1, x2[, axis]) |
interquartile range of error |
maxabs(x1, x2[, axis]) |
maximum absolute error |
meanabs(x1, x2[, axis]) |
mean absolute error |
medianabs(x1, x2[, axis]) |
median absolute error |
medianbias(x1, x2[, axis]) |
median bias, median error |
mse(x1, x2[, axis]) |
mean squared error |
rmse(x1, x2[, axis]) |
root mean squared error |
stde(x1, x2[, ddof, axis]) |
standard deviation of error |
vare(x1, x2[, ddof, axis]) |
variance of error |