XGBoost
E427706
XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
All labels observed (1)
| Label | Occurrences |
|---|---|
| XGBoost canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T4279687 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XGBoost Context triple: [Vertex AI, supports, XGBoost]
-
A.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
B.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
C.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
D.
Pegasos II
Pegasos II is a PowerPC-based computer mainboard developed by Genesi that became popular as a hardware platform for alternative operating systems such as AmigaOS and MorphOS.
-
E.
Apache Mahout
Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: XGBoost Target entity description: XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
-
A.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
B.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
C.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
D.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
-
E.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
- F. None of above. chosen
Statements (79)
| Predicate | Object |
|---|---|
| instanceOf |
gradient boosting library
ⓘ
machine learning library ⓘ open-source software ⓘ software project ⓘ |
| developer |
Tianqi Chen
NERFINISHED
ⓘ
contributors from the open-source community ⓘ |
| feature |
GPU acceleration
ⓘ
column block structure for parallel learning ⓘ custom evaluation metrics ⓘ custom objective functions ⓘ distributed training ⓘ early stopping ⓘ handling missing values ⓘ out-of-core computation ⓘ regularization ⓘ sparse aware learning ⓘ tree pruning ⓘ weighted quantile sketch ⓘ |
| hyperparameter |
alpha
ⓘ
colsample_bytree ⓘ gamma ⓘ lambda ⓘ learning_rate ⓘ max_depth ⓘ n_estimators ⓘ subsample ⓘ |
| license | Apache License 2.0 ⓘ |
| notableProperty |
often achieves state-of-the-art performance on tabular datasets
ⓘ
robust to missing values in features ⓘ supports GPU-accelerated training ⓘ supports distributed training on clusters ⓘ supports parallel tree construction ⓘ |
| optimizationGoal |
high performance
ⓘ
memory efficiency ⓘ scalability ⓘ |
| partOf | DMLC (Distributed Machine Learning Community) projects NERFINISHED ⓘ |
| primaryApplication |
binary classification
ⓘ
classification ⓘ multiclass classification ⓘ ranking ⓘ regression ⓘ survival analysis ⓘ time series forecasting (with feature engineering) ⓘ |
| programmingLanguage | C++ ⓘ |
| repository | https://github.com/dmlc/xgboost ⓘ |
| supportsBooster |
dart
ⓘ
gblinear ⓘ gbtree ⓘ |
| supportsDataFormat |
CSV
ⓘ
DMatrix NERFINISHED ⓘ LibSVM format NERFINISHED ⓘ NumPy arrays ⓘ Pandas DataFrame ⓘ |
| supportsDataType |
sparse matrices
ⓘ
structured data ⓘ tabular data ⓘ |
| supportsLanguageBinding |
C
NERFINISHED
ⓘ
CLI NERFINISHED ⓘ Dask NERFINISHED ⓘ Java NERFINISHED ⓘ Julia NERFINISHED ⓘ Python NERFINISHED ⓘ R NERFINISHED ⓘ Scala NERFINISHED ⓘ Spark NERFINISHED ⓘ |
| supportsLearningTask |
gradient boosted decision trees
ⓘ
linear models ⓘ supervised learning ⓘ tree-based models ⓘ |
| supportsObjective |
binary:logistic
ⓘ
multi:softmax ⓘ multi:softprob ⓘ rank:map ⓘ rank:ndcg ⓘ rank:pairwise ⓘ reg:squarederror ⓘ |
| usedFor |
Kaggle competitions
ⓘ
industry machine learning systems ⓘ |
| website | https://xgboost.ai NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: XGBoost Description of subject: XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.