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Finally, despite the SuperLearner procedure, which is intended to optimize the prediction, our predictive performance was in fact limited, with an AUROC of 0.760 955% CI 0.694, 0.826) for the SuperLearner weighted algorithm.
We also propose to jointly optimize the prediction filters by using an algorithm that alternates between filter optimization and weight computation.
Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights.
This algorithm first predicts the values, checks for the error and then tries to optimize the prediction.
The most commonly used approaches optimize the prediction filters by minimizing the variance of the detail coefficients.
Our proposed approach combines Support Vector Regression and Hodrick Prescott filter in order to optimize the prediction of stock price.
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As false negatives are deemed to be more important in any experiment, misclassification cost was set for false negatives and was incremented serially so as to optimize the predictions.
The mean and coefficient of variation (S.D./mean * 100%) were calculated for each grid to optimize the predictions and examine the output stability.
Bayesian logistic regression utilizes the best features of both methods by employing the prior information about the success probability and recursively optimizing the prediction parameters to achieve the optimal classification/prediction rather than simple regression coefficient for classifiers [ 42- 44].
A joint optimization method can therefore be proposed which iteratively optimizes the prediction filters p j ( H H ), p j ( H L ), and p j ( L H ). While the optimization of the prediction filters p j ( H L ) and p j ( L H ) is simple, the optimization of the prediction filter p j ( H H ) is less obvious.
The second method consists of optimizing the prediction and update filters as proposed in [20, 38].
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