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
Fit metrics are computed from all time points in
y
.For models estimated with
test=False
, the entirey_pred
is predicted with the averaged TRF from the different runs (with each run corresponding to one validation set).For models estimated with
test=True
,y_pred
in each test segment is predicted from the averaged TRF from the corresponding training runs (each nontest segment used as validation set once), and the different test segments are then concatenated to compute the fitmetrics in comparison withy
.
 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
. Note thatr
does not take into account the model’s ability to predict the magnitude of the response, only its shape; for a measure that reflects both, consider usingproportion_explained
.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 variation in
y
that is explained by the model. Calculated as1  (variation(residual) / variation(y))
. Variation is caculated as thel1
orl2
norm, depending on theerror
that was used for model fitting. Note that this does not correspond tor**2
even forerror='l2'
, because residuals are not guaranteed to be orthogonal to predictions.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 