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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
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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.
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Distribution-Preserving Stratified Sampling for Learning Problems.

Cristiano Cervellera, Danilo Maccio

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    This study introduces a new stratified sampling algorithm to create representative data samples for machine learning. The method ensures selected data closely mirrors the original distribution, improving model training and generalization.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Probability Theory

    Background:

    • Data sampling is crucial for machine learning tasks like storage, computational efficiency, and creating training/validation sets.
    • Maintaining the original data distribution in samples is vital for both unsupervised and supervised learning to ensure accurate insights and minimize generalization error.

    Purpose of the Study:

    • To analyze stratified sampling using probability distances.
    • To develop an algorithm for creating data samples that closely resemble the original data distribution.
    • To introduce an adaptive version for handling streaming data.

    Main Methods:

    • Analysis of stratified sampling through the lens of distances between probabilities.
    • Development of a recursive binary partitioning algorithm for the input space.
    • Theoretical analysis to prove greedy optimality and derive error bounds.
    • Introduction of an adaptive algorithm for streaming data scenarios.

    Main Results:

    • A novel algorithm for stratified sampling is presented.
    • Theoretical guarantees of greedy optimality and explicit error bounds are established.
    • An adaptive version effectively handles streaming data.

    Conclusions:

    • The proposed algorithm provides an effective method for obtaining representative data samples.
    • The theoretical analysis validates the algorithm's performance and optimality.
    • The adaptive approach extends its utility to dynamic, streaming data environments.