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Survival Tree01:19

Survival Tree

84
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.
 Building a Survival Tree
Constructing a...
84
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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相关实验视频

Updated: Jul 1, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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树值:回归树的选择性推理

Anna C Neufeld1, Lucy L Gao2, Daniela M Witten3

  • 1Department of Statistics, University of Washington, Seattle, WA 98195, USA.

Journal of machine learning research : JMLR
|March 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的选择性推断框架,用于分类和回归树 (CART). 这些方法确保可靠的统计保证,用于推断CART模型输出,控制错误率和覆盖范围.

关键词:
汽车车上的车辆.回归树是一种回归树.假设测试 测试 假设测试选择后的推断推断.选择性推论是选择性的推论.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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相关实验视频

Last Updated: Jul 1, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 对分类和回归树的推理 (CART) 需要专门的方法.
  • 纯粹的推断方法无法提供像1型错误控制这样的标准统计保证.

研究的目的:

  • 为适配的CART模型开发一个选择性推理框架.
  • 确保对CART输出进行有效的统计推断,包括错误率控制和覆盖范围.

主要方法:

  • 通过对用于树木估计的数据进行条件化,提出一个选择性推断框架.
  • 开发一种测试,用于控制选择性1型错误率的终端节点之间的平均响应差异.
  • 在终端节点内创建平均响应的置信区间,实现名义选择性覆盖.
  • 提供有效的算法来计算必要的条件集.

主要成果:

  • 拟议的框架成功地控制了假设测试的选择性1型错误率.
  • 置信区间实现了终端节点内的平均响应的标称选择性覆盖.
  • 方法通过模拟研究和对现实世界数据集的应用来验证.

结论:

  • 选择性推断框架为分析CART模型提供了一个统计学上合理的方法.
  • 这些方法在机器学习算法中提高了统计推理的可靠性.
  • 该方法适用于各种数据集,包括健康和营养研究中的数据集.