gbm

GBM is an acronym that can refer to two different fields: medicine and machine learning. In medicine, GBM stands for glioblastoma multiforme, a highly aggressive brain tumor. In computer science, GBM stands for gradient boosting machine, an ensemble learning technique used for predictive modeling. The intended meaning is usually clear from context.

Glioblastoma multiforme is the most common and aggressive primary malignant brain tumor in adults, classified as a grade IV astrocytoma by the World Health Organization. Symptoms include headaches, seizures, cognitive or focal deficits, and personality or mood changes. Diagnosis involves magnetic resonance imaging and histopathology. Treatment typically combines surgical resection when possible with radiotherapy and temozolomide chemotherapy. Despite this multimodal approach, prognosis remains poor, with median survival around 12 to 15 months and frequent recurrence. Research continues to explore targeted therapies, immunotherapy, and advances in radiotherapy and imaging to improve outcomes and quality of life.

Gradient boosting machine is a class of algorithms that build an ensemble of weak predictors, usually decision trees, in sequence. Each new model aims to correct the errors of the previous ones, using gradient descent to minimize a loss function. GBMs often achieve high accuracy on structured data and are widely used for classification and regression tasks. Popular implementations include XGBoost, LightGBM, and CatBoost. Advantages include strong predictive performance and flexibility with heterogeneous data; limitations include training time and memory requirements, risk of overfitting without careful tuning, and comparatively lower interpretability than simpler models. GBMs are widely used in industry and research for predictive analytics and ranking tasks.