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相关概念视频

Interpreting R Charts01:22

Interpreting R Charts

314
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Survival Tree01:19

Survival Tree

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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.
 Building a Survival Tree
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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基于集群的随机森林可视化和解释.

Max Sondag, Christofer Meinecke, Dennis Collaris

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    这项研究引入了一种新的可视化方法,以提高随机森林的解释性,即机器学习技术. 通过对类似的决策树进行集群,用户可以理解模型性能,而无需单独分析每个树.

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    科学领域:

    • 机器学习 机器学习
    • 数据可视化 数据可视化
    • 人工智能的人工智能

    背景情况:

    • 随机森林是强大的机器学习模型用于分类,但由于其复杂的结构,其解释性较低.
    • 在随机森林中解释个别决策树通常是无法理解整体模型行为的.

    研究的目的:

    • 开发一种新的可视化系统和方法,以提高随机森林的解释性.
    • 为了使用户能够掌握随机森林模型的一般性能,而不必检查每个决策树.

    主要方法:

    • 引入了一个新的距离度量来对决策树进行聚类,同时考虑决策规则和预测.
    • 开发了两个可视化技术:特征图 (可视化特征拓) 和规则图 (可视化决策规则).
    • 通过使用"Glass"数据集和用户研究来评估该方法.

    主要成果:

    • 提出的集群和可视化方法有效地代表了随机森林的集体行为.
    • 用户可以通过可视化集群和单个树结构获得对模型性能的见解.
    • 新的距离度量有意义地将类似的决策树组合在一起.

    结论:

    • 开发的可视化系统显著提高了随机森林的解释性.
    • 将类似的决策树集群为理解复杂的机器学习模型提供了可扩展的方法.
    • 功能图和规则图为分析决策树结构和规则提供了有价值的工具.