Sentence examples for boosting modeling from inspiring English sources

The objective of this work was to compare three spatially explicit modeling approaches for soil depth (SD) for France (540 K km2), produced using: i) a straight forward digital soil mapping (DSM) approach, based on regression treemodeling (RTM), ii) gradient boosting modeling (GBM), and iii) multi-resolution kriging (MrK) for large datasets.

Models for estimating the propensity score equation have included parametric logit regression with chosen interaction and polynomial terms (e.g., Dehejia and Wahba 1999; Hirano and Imbens 2001a), and generalized boosting modeling (McCaffrey et al. 2004), to name a few.

In gradient boosting modeling, more accurate estimate of the response variable is obtained through consecutively fitting new models in order to reduce the variance between the predicted and observed responses.

The system is constructed based on three components namely, trajectory reconstruction, geo-fencing analysis, and gradient boosting modelling.

In addition, this paper shows that considering Particulate Matter (PM) as a parameter in estimation of GSR boosts modeling efficiency.

The gradient boosting model-based solution provides better predictions compared with the linear model benchmark solution.

Although chances of overfitting are reduced using LOOCV and boosting models,11 current results are preliminary and require replication by independent research groups, in larger samples.

This paper introduces two hybrid models, i.e. PCA with bagging and PCA with Bayesian boosting models for feature based opinion classification of product reviews.

(2) Unlike the weak classifiers in the boosting models, these selected ε-ball features are used to explain object in a generative way and are mutually independent.

Scaling discrete AdaBoost to handle real-valued weak hypotheses has often been done under the auspices of convex optimization, but little is generally known from the original boosting model standpoint.

The best individual performance has been observed by the stochastic gradient boosting model followed by Cubist, random forest and model averaged neural networks which except for the latter are all regression tree-based algorithms.