class eelbrain.BoostingResult(y, x, tstart, tstop, scale_data, delta, mindelta, error, basis, basis_window, partitions_arg, partitions, model, prefit, h, r, isnan, spearmanr, residual, t_run, y_mean, y_scale, x_mean, x_scale, y_info={}, r_l1=None, selective_stopping=0, n_samples=None, **debug_attrs)

Result from boosting a temporal response function

h : NDVar | tuple of NDVar

The temporal response function. Whether h is an NDVar or a tuple of NDVars depends on whether the x parameter to boosting() was an NDVar or a sequence of NDVars.

h_scaled : NDVar | tuple of NDVar

h scaled such that it applies to the original input y and x. If boosting was done with scale_data=False, h_scaled is the same as h.

h_source : NDVar | tuple of NDVar

If h was constructed using a basis, h_source represents the source of h before being convolved with the basis.

h_time : UTS

Time dimension of the kernel.

r : float | NDVar

Correlation between the measured response and the response predicted with h. Type depends on the y parameter to boosting().

spearmanr : 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.

residual : float | NDVar

The residual of the final result

  • error='l1': the sum of the absolute differences between y and h * x.
  • error='l2': the sum of the squared differences between y and h * 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.

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.

partitions : int

Numbers of partitions of the data used for cross validation.