Home ›
Entities
› academia
› Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Summary
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest is a scholarly article[1].
Key Facts
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. Retrieved May 24, 2026, from https://4ort.xyz/entity/assessing-the-predictive-capability-of-ensemble-tree-methods-for-landslide-susceptibility-mapping-using-xgboost-gradient
MLA“Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/assessing-the-predictive-capability-of-ensemble-tree-methods-for-landslide-susceptibility-mapping-using-xgboost-gradient.
BibTeX@misc{4ortxyz_assessing-the-predictive-capability-of-ensemble-tree-methods-for-landslide-susceptibility-mapping-using-xgboost-gradient_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest}}, year = {2026}, url = {https://4ort.xyz/entity/assessing-the-predictive-capability-of-ensemble-tree-methods-for-landslide-susceptibility-mapping-using-xgboost-gradient}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest — https://4ort.xyz/entity/assessing-the-predictive-capability-of-ensemble-tree-methods-for-landslide-susceptibility-mapping-using-xgboost-gradient (retrieved 2026-05-24)
