# mvpa2.misc.errorfxΒΆ

Error functions helpers.

PyMVPA can use arbitrary function which takes 2 arguments: predictions and targets and spits out a scalar value. Functions below are for the convinience, and they confirm the agreement that ‘smaller’ is ‘better’

Functions

 auc_error(predicted, target) Computes the area under the ROC for the given the target and predicted to make the prediction. corr_error(predicted, target) Computes the correlation between the target and the predicted values. corr_error_prob(predicted, target) Computes p-value of correlation between the target and the predicted values. correlation(predicted, target) Computes the correlation between the target and the predicted values. match_accuracy(predicted, target) Computes number of matches between some target and some predicted values. mean_fnr(predicted, target) Mean False Negative Rate (FNR) = 1 - TPR mean_match_accuracy(predicted, target) Computes mean of number of matches between some target and some predicted values. mean_mismatch_error(predicted, target) Computes the percentage of mismatches between some target and some predicted values. mean_power_fx(data) Returns mean power mean_tpr(predicted, target) Mean True Positive Rate (TPR). mismatch_error(predicted, target) Computes number of mismatches between some target and some predicted values. pearsonr(x, y) Calculates a Pearson correlation coefficient and the p-value for testing non-correlation. prediction_target_matches(predicted, target) Returns a boolean vector of correctness of predictions relative_rms_error(predicted, target) Ratio between RMSE and root mean power of target output. rms_error(predicted, target) Computes the root mean squared error of some target and some predicted values. root_mean_power_fx(data) Returns root mean power trapz(y[, x, dx, axis]) Integrate along the given axis using the composite trapezoidal rule. variance_1sv(predicted, target) Ratio of variance described by the first singular value component.