eelbrain.BoostingResult
 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
 Variables
h (NDVar  tuple of NDVar) – The temporal response function (the average of all TRFs from the different runs/partitions). Whether
h
is anNDVar
or atuple
ofNDVar
depends on whether thex
parameter toboosting()
was anNDVar
or a sequence ofNDVar
.h_scaled (NDVar  tuple of NDVar) –
h
scaled such that it applies to the original inputy
andx
. If boosting was done withscale_data=False
,h_scaled
is the same ash
.h_source (NDVar  tuple of NDVar) – If
h
was constructed using a basis,h_source
represents the source ofh
before being convolved with the basis.h_time (UTS) – Time dimension of the kernel.
r (float  NDVar) – Correlation between the measured response
y
and the predicted responseh * x
. When using crossvalidation (callingboosting()
withtest=True
), each partition ofy
is predicted using theh
estimated from the corresponding training partitions. Otherwise, all ofy
is estimated using the averageh
. For vector data, measured and predicted responses are normalized, andr
is computed as the average dot product over time. The type ofr
depends on they
parameter toboosting()
: Ify
is onedimensional,r
is scalar, otherwise it is aNDVar
.r_rank (float  NDVar) – As
r
, the Spearman rank correlation.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 betweeny
andh * x
.error='l2'
: the sum of the squared differences betweeny
andh * x
.
For vector
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.
proportion_explained (float  NDVar) – The proportion of the explained variability. Variability is caculated as
l1
orl2
norm, depending on theerror
that was used for model fitting. Forl2
, it corresponds to the proportion of variance explained. Calculated as1  (residual / variability)
.scale_data (bool) – Scale_data parameter used.
y_mean (NDVar  scalar) – Mean that was subtracted from
y
.y_scale (NDVar  scalar) – Scale by which
y
was divided.x_mean (NDVar  scalar  tuple) – Mean that was subtracted from
x
.x_scale (NDVar  scalar  tuple) – Scale by which
x
was divided.splits (Splits) – Data splits used for crossvalidation. Use
splits.plot()
to visualize the crossvalidation scheme.partition_results (list of BoostingResuls) – If
boosting()
is called withpartition_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
1: results from before this attribute was added
0: Normalize
x
after applying basis1: Numba implementation
2: Cython multiprocessing implementation (Eelbrain 0.38)
Methods

Predict responses to 
Results from the different test partitions in a 