Sentence examples for using a regression tree from inspiring English sources

Pedotransfer functions were built using a regression tree model on a subset of the data for which total Si concentration was measured.

Neuropathy incidence and severity were curated from drug labels and literature and were used to build a predictive model of DIPN using a regression tree algorithm, based on the drug targets and their intermediators.

The ADHERE registry developed the CART method using a regression tree analysis, which defined three sequential parameters: BUN (blood urea nitrogen) (above 43 mg/dl), systolic arterial pressure (below 115 mmHg) and seric creatinine (above 2.75 mg/dl).

Each element of the proximity matrix takes the value 1 or 0. If two mRNAs, say a and b, belong to the same terminal node, then [a, b] = 1, else [a, b] = 0. Though the proximity matrix obtained using a regression tree has low prediction accuracy, the use of a collection of trees greatly improves the prediction accuracy [ 20].

A supervised classification is performed for the building and the RSU scales using a regression trees model based on these indicators and on 10 urban fabric typological classes defined by urbanists and architects.

We used a regression tree model to examine the nonlinear relationships between LST and each of three satellite-based indices within the UHI clusters: normalized differential vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized difference bareness index (NDBaI).

In this work, we used a regression tree analysis to explain the effects of each profile on the total care cost [ 32].

Using a regression class tree, the Gaussian components are grouped into classes with each class having its own transformation matrix.

In order to generate a consensus prediction exploiting the diversities of each method, we combined the results obtained by each category of method in an optimized predictor using a regression model tree (48).

The remotely sensed variables were collapsed using a principal components analysis, and combined with the canopy segment summary variables and topographic descriptors, and field survey data to explain the variation in the initial sample of basal area (BA) using a regression model (models to predict trees per hectare (TPH) and percent conifer BA were also developed).

We next attempted to identify the most appropriate blast cut-point using a survival regression tree approach.