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 |
| 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 |
| 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_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 |
| 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 |
| 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. |
