Weighted quantile sketch
A weighted quantile sketch is an approximate algorithm for estimating quantiles or quantile-based split points while accounting for item weights.
Ensemble Methods for Machine Learning
A weighted quantile sketch is an approximate data-structuring algorithm for estimating quantile-based split points while accounting for example weights.
- How it is used
- Speeds XGBoost tree training by approximating feature split selection for large-scale Newton boosting.
- How deep it goes
- Explains weighted split selection, feature-dependent buckets, and scalability, but defers the considerable implementation details.
- Examples
- Uses feature-dependent buckets, Hessian-weighted examples, and large-dataset XGBoost training as concrete illustrations.
- What you can do after
- Evaluate approximate split finding as an efficiency tradeoff in XGBoost and distinguish it from ordinary histogram binning.
Read how Ensemble Methods for Machine Learning explains weighted quantile sketch →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
An approximate quantile algorithm that uses weights, such as gradient statistics, to identify candidate split points.
- How it is used
- Accelerates gradient-boosted tree split finding by approximating candidate thresholds from weighted gradient statistics.
- How deep it goes
- Explains weighted candidates, histogram acceleration, and bin reuse, without deriving the sketch’s internal quantile construction.
- Examples
- Compares exact and 256-bin histogram splitting on latitude using matching thresholds and sharply different runtimes.
- What you can do after
- Evaluate XGBoost split-finding choices by comparing exact, approx, and hist methods and their bin-reuse behavior.
Related concepts
- XGBoostused by · XGBoost is an open-source software library for efficient gradient-boosted decision-tree algorithms used in supervised machine learning.
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