- class eelbrain.BoostingResult(y, x, tstart, tstop, scale_data, delta, mindelta, error, selective_stopping, y_mean, y_scale, x_mean, x_scale, _h, _isnan, t_run, basis, basis_window, splits=None, n_samples=None, _y_info=<factory>, _y_dims=None, i_test=None, residual=None, r=None, r_rank=None, r_l1=None, partition_results=None, version=13, algorithm_version=-1, y_pred=None, fit=None, prefit=None)
Result from boosting a temporal response function
h (NDVar | tuple of NDVar) – The temporal response function (the average of all TRFs from the different runs/partitions). Whether
NDVardepends on whether the
NDVaror a sequence of
h_scaled (NDVar | tuple of NDVar) –
hscaled such that it applies to the original input
x. If boosting was done with
h_scaledis the same as
h_source (NDVar | tuple of NDVar) – If
hwas constructed using a basis,
h_sourcerepresents the source of
hbefore being convolved with the basis.
h_time (UTS) – Time dimension of the kernel.
r (float | NDVar) – Correlation between the measured response
yand the predicted response
h * x. When using cross-validation (calling
test=True), each partition of
yis predicted using the
hestimated from the corresponding training partitions. Otherwise, all of
yis estimated using the average
h. For vector data, measured and predicted responses are normalized, and
ris computed as the average dot product over time. The type of
rdepends on the
ris scalar, otherwise it is a
t_run (float) – Time it took to run the boosting algorithm (in seconds).
error (str) – The error evaluation method used.
The residual of the final result
error='l1': the sum of the absolute differences between
h * x.
error='l2': the sum of the squared differences between
h * x.
y, the error is defined based on the distance in space for each data point.
delta (scalar) – Kernel modification step used.
mindelta (None | scalar) – Mindelta parameter used.
n_samples (int) – Number of samples in the input data time axis.
scale_data (bool) – Scale_data parameter used.
y_mean (NDVar | scalar) – Mean that was subtracted from
y_scale (NDVar | scalar) – Scale by which
splits (Splits) – Data splits used for cross-validation. Use
splits.plot()to visualize the cross-validation scheme.
partition_results (list of BoostingResuls) – If
boosting()is called with
partition_results=True, this attribute contains the results for the individual test paritions.
algorithm_version (int) – Version of the algorithm with which the model was estimated;
-1for results from before this attribute was added.