Models assessed on sparse datasets with sample size sequentially increasing twofold. Amount of sparsity varied on a per-instance basis from 1 to 6 missing features, mirroring the quantity of sparsity of the parent subset. Distribution of sparsity was real-world, mirroring parent subsets. Lines represent metric mean across 10 simulations; shaded bands represent 99% confidence intervals.</p
A. Decomposing model behavior into two metrics. We examined model behavior along two specific aspect...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
(a) Plots of model vs. observer performance (proportion correct), averaged across observers (left su...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 fe...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–8 of 9 feat...
Each point indicates a model performance estimate in the training set (X axis) and its gap from the ...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
Performance of sparse and non-sparse discriminant models in internal validation compared on the same...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
Performance of county-level models using the baseline (“Restricted”) predictor set, plotted against ...
Model performance estimate and generalization gap according to the sample size and the level of task...
<p>Results of simulations on the two datasets for the percent correctly classified when the size of ...
The pioneering work on parameter orthogonalization by Cox and Reid (1987) is presented as an inducem...
A, B, C, and D: Each colored line indicates a result from a single random training-test set split, a...
A. Decomposing model behavior into two metrics. We examined model behavior along two specific aspect...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
(a) Plots of model vs. observer performance (proportion correct), averaged across observers (left su...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 fe...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–8 of 9 feat...
Each point indicates a model performance estimate in the training set (X axis) and its gap from the ...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
Performance of sparse and non-sparse discriminant models in internal validation compared on the same...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
Performance of county-level models using the baseline (“Restricted”) predictor set, plotted against ...
Model performance estimate and generalization gap according to the sample size and the level of task...
<p>Results of simulations on the two datasets for the percent correctly classified when the size of ...
The pioneering work on parameter orthogonalization by Cox and Reid (1987) is presented as an inducem...
A, B, C, and D: Each colored line indicates a result from a single random training-test set split, a...
A. Decomposing model behavior into two metrics. We examined model behavior along two specific aspect...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
(a) Plots of model vs. observer performance (proportion correct), averaged across observers (left su...