<p>Results for feature selection, model selection and validation, using the two selection criteria and the four data partitioning schemes. The number of features for the and models is shown (#), alongside their leave-one-out cross-validation correlations and RMSE. The RMSE and correlation of the values used for selecting these models is also shown, as are those when the model is applied to the validation set, along with the significance of correlation.</p
Model performances for models trained on all features restricted to tp0, tp1, tp2, tp0 + tp1 and all...
For each validation set the following metrics were calculated: RMSE, Pearson’s correlation coefficie...
<p>Five-fold cross-validation of the linear (blue bars) and the nonlinear (yellow bars) models on th...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>Note. <i>r</i> = correlations between scores predicted by the model and score for that tweet from...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
The results from the 10 times 20% out LPDM cross-validations of the three modelling approaches appli...
Comparison results of different network models: A is training accuracy of model, B is validation acc...
<p>Analysis of effects of different similarity measures—Pearson Correlation results for 10-fold cros...
<p>First, different models are trained and validated with cross-validation and the best set of param...
Weighted F scores are reported on the validation set (20% HumVD-train) for all algorithms. ‘Sequence...
The “inner” cross-validation: The “inner” cross-validation is for model selection based on their acc...
<p>The mean squared error (MSE) for the model with highest is given for both the training set (90% ...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
Model performances for models trained on all features restricted to tp0, tp1, tp2, tp0 + tp1 and all...
For each validation set the following metrics were calculated: RMSE, Pearson’s correlation coefficie...
<p>Five-fold cross-validation of the linear (blue bars) and the nonlinear (yellow bars) models on th...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>Note. <i>r</i> = correlations between scores predicted by the model and score for that tweet from...
<p>Empirical significance was obtained from the fraction of permutations that showed a correlation h...
The results from the 10 times 20% out LPDM cross-validations of the three modelling approaches appli...
Comparison results of different network models: A is training accuracy of model, B is validation acc...
<p>Analysis of effects of different similarity measures—Pearson Correlation results for 10-fold cros...
<p>First, different models are trained and validated with cross-validation and the best set of param...
Weighted F scores are reported on the validation set (20% HumVD-train) for all algorithms. ‘Sequence...
The “inner” cross-validation: The “inner” cross-validation is for model selection based on their acc...
<p>The mean squared error (MSE) for the model with highest is given for both the training set (90% ...
Results of sensitivity analyses across different splits of the training and test sets. We created 1,...
<p>Subjects were randomly assigned to the training or validation set. All training, including tuning...
Model performances for models trained on all features restricted to tp0, tp1, tp2, tp0 + tp1 and all...
For each validation set the following metrics were calculated: RMSE, Pearson’s correlation coefficie...
<p>Five-fold cross-validation of the linear (blue bars) and the nonlinear (yellow bars) models on th...