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.