Bagging and Bootstrap Sampling
Bagging, short for bootstrap aggregation, is an ensemble technique that builds multiple models—most commonly decision trees—by training each model on a different random sample of the data. These samples are drawn with replacement, a process known as bootstrap sampling.
Bootstrap sampling is a statistical method where samples are drawn from the dataset with replacement, allowing the same data point to appear multiple times in a sample.
Decision tree is a tree-structured model used for classification or regression, where each internal node splits the data based on a feature.
By averaging the predictions of these models, bagging reduces variance and increases stability, particularly for high-variance models such as DecisionTreeClassifier. This averaging effect means that while individual models might overfit to their specific bootstrap samples, their combined output is more robust and less sensitive to the quirks of any single sample.
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Can you explain what "with replacement" means in this context?
Why does bagging work particularly well with high-variance models?
Can you give an example of how bagging is used in practice?
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Completion arvosana parantunut arvoon 7.14Bagging and Bootstrap Sampling
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Bagging, short for bootstrap aggregation, is an ensemble technique that builds multiple models—most commonly decision trees—by training each model on a different random sample of the data. These samples are drawn with replacement, a process known as bootstrap sampling.
Bootstrap sampling is a statistical method where samples are drawn from the dataset with replacement, allowing the same data point to appear multiple times in a sample.
Decision tree is a tree-structured model used for classification or regression, where each internal node splits the data based on a feature.
By averaging the predictions of these models, bagging reduces variance and increases stability, particularly for high-variance models such as DecisionTreeClassifier. This averaging effect means that while individual models might overfit to their specific bootstrap samples, their combined output is more robust and less sensitive to the quirks of any single sample.
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