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相关概念视频

Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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一个端到端的决策森林推断管道的比较

Hong Guan1, Saif Masood1, Mahidhar Dwarampudi1

  • 1Arizona State University.

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|September 5, 2025
PubMed
概括

本研究介绍了决策森林的数据库内推断设计,其性能优于现有的脱和数据库内方法. 这种以关系为中心的方法在大型机器学习模型中表现出卓越的性能.

关键词:
决策森林机器学习系统

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科学领域:

  • 机器学习
  • 数据库系统
  • 数据管理

背景情况:

  • 决策森林 (RandomForest,XGBoost,LightGBM) 在表格数据方面表现出色.
  • 现有的推断框架 (ONNX,TreeLite,TF-DF,HummingBird,Nvidia FIL,lleaves) 与数据库脱而出,导致性能开销.

研究的目的:

  • 开发和评估决策森林的数据库推断系统.
  • 使用DICT模型分析脱和数据库内推断之间的性能差距.
  • 在数据库中比较以UDF为中心和以关系为中心的表示.

主要方法:

  • 开发了一个DICT模型来量化性能差异.
  • 实施和优化了以UDF为中心和以关系为中心的数据库内推断方法.
  • 与分离框架和现有的数据库解决方案 (SparkSQL,PostgresML) 进行了全面的基准测试.

主要成果:

  • DICT模型准确地反映了业绩差异.
  • 在数据库中以关系为中心的表示方法显著优于以UDF为中心的大型模型.
  • 拟议的数据库内推断设计在所有评估的基线上都显示出卓越的性能.

结论:

  • 对决策森林的数据库内推断提供了显著的性能优势.
  • 在数据库中优化大模型推理的关键是以关系为中心的表示.
  • 这项工作弥合了机器学习推断和高效数据管理之间的差距.