Multivariate Pattern Analysis in Python

# 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_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.
`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
`variance_1sv`(predicted, target) | Ratio of variance described by the first singular value component.