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The Random Forest predictor is computed by averaging the tree predictors over trees for which the given observation was OOB.
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The possibilities of model interpretation were explored: the relative influence of each predictor was computed, and the partial dependence plots were obtained.
To determine statistical difference in prediction, previous and current ALSPAC (factor: group membership) were combined and the interaction between group membership and individual predictor was computed.
The "76-gene" predictor was computed as described by the authors, with one exception: a 90% sensitivity threshold was used to establish cut-off values for high and low risk tumors in the training sets rather than a 100% threshold since some tumors had outlying values in some data sets and this slightly lower threshold enhanced the performance of the classifier [ 10].
Additionally, the F1- label score used by Briesemeister et al. [ 26] to evaluate the performance of multi-location predictors is computed as: F 1 -label = 1 | S | ∑ s i ∈ S 2 × Pre s i × Rec s i Pre s i + Rec s i.
A preliminary selection of the predictors was computed excluding those with an AUC < 0.55.
Thus, a predictive analysis of the outcome "institutionalization", using the baseline data as potential predictors, was computed to test H6.
The most suitable variance covariance structure was detected (Factor Analytic Order 1) and genetic predictors were computed for a step size of 4 cM.
The probabilities related to all other possible combinations (71 in all) of the values of these predictors were computed and the % variations between each combination and the most favourable situation were calculated.
The score for admission glycemia > 218 mg/dl was arbitrarily established to be 5, while scores of the remaining predictors were computed according to the ratio between odds ratios of glycemia > 218 mg/dl and that of individual predictors obtained by the logistic regression analysis and were approximated to the unit.
The final prediction is computed as the weighted combination of each predictor.
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