# mvpa2.misc.errorfx¶

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.