Performance assessment of prediction model based on different criteria; (a) comparison of statistical parameters for training and validation data; (b) ± 5% error bound for prediction model; (c) comparison of experimental data, GEP model data and absolute error; (d) comparison of experimental data and GEP prediction model data against training and validation data.</p
When performing a regression or classification analysis, one needs to specify a statistical model. T...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
<p>The experiment was conducted 10 times using 10-fold cross-validation performed on the training se...
<p>Comparison prediction accuracy of model with <i>p</i> = 1000 and <i>p</i> = 200.</p
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
Overall performance and calibration of the prediction models (quantitative approach).</p
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>The differences in the prediction errors are plotted as squares, where a negative value denotes b...
<p>The prediction errors from the MS and SS models are plotted as circles and triangles, respectivel...
Performance metrics (r2, AUC) and their standard deviations are computed on an independent test set....
Comparisons of the prediction performance of Catboost model and logistic regression model.</p
Performance metrics of the prediction models using logistic regression and random forest methods wit...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
A: α = 0.3; β = ±log(1.5). B: α = 0.3; β = ±log(1.75). C: α = 0.5; β = ±log(1.5). D: α = 0.5; β = ±l...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
<p>The experiment was conducted 10 times using 10-fold cross-validation performed on the training se...
<p>Comparison prediction accuracy of model with <i>p</i> = 1000 and <i>p</i> = 200.</p
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
Overall performance and calibration of the prediction models (quantitative approach).</p
<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p
<p>The differences in the prediction errors are plotted as squares, where a negative value denotes b...
<p>The prediction errors from the MS and SS models are plotted as circles and triangles, respectivel...
Performance metrics (r2, AUC) and their standard deviations are computed on an independent test set....
Comparisons of the prediction performance of Catboost model and logistic regression model.</p
Performance metrics of the prediction models using logistic regression and random forest methods wit...
textabstractThe performance of prediction models can be assessed using a variety of methods and metr...
A: α = 0.3; β = ±log(1.5). B: α = 0.3; β = ±log(1.75). C: α = 0.5; β = ±log(1.5). D: α = 0.5; β = ±l...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...