adelie.cv.CVGrpnetResult#
- class adelie.cv.CVGrpnetResult(lmdas: ndarray, losses: ndarray, avg_losses: ndarray, best_idx: int)[source]#
Result of K-fold CV group elastic net.
Methods
__init__
(lmdas, losses, avg_losses, best_idx)fit
(*[, lmda_path_size])Fits group elastic net until the best CV \(\lambda\).
Plots the average K-fold CV loss.
Attributes
The common regularization path used for all folds.
losses[k,i]
is the CV loss when validating on foldk
atlmdas[i]
.avg_losses[i]
is the average CV loss atlmdas[i]
.Argmin of
avg_losses
.- fit(*, lmda_path_size: int = 100, **grpnet_params)[source]#
Fits group elastic net until the best CV \(\lambda\).
- Parameters:
- lmda_path_sizeint, optional
Number of regularizations in the path. Default is
100
.- **grpnet_paramsoptional
Parameters to
adelie.solver.grpnet()
.
- Returns:
- state
Result of calling
adelie.solver.grpnet()
.
See also
- plot_loss()[source]#
Plots the average K-fold CV loss.
For each fitted \(\lambda\), the average K-fold CV loss as well as an error bar of one standard deviation (above and below) is plotted.
- lmdas: ndarray#
The common regularization path used for all folds.
- losses: ndarray#
losses[k,i]
is the CV loss when validating on foldk
atlmdas[i]
.
- avg_losses: ndarray#
avg_losses[i]
is the average CV loss atlmdas[i]
.
- best_idx: int#
Argmin of
avg_losses
.