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💬 프롬프트 라이브러리 📖 AI 용어 사전 🔗 유용한 링크

AI 용어집

인공지능 완전 사전

162
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23,060
용어
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Out-of-Bag Score

Performance metric derived from the out-of-bag error, often expressed as 1 minus the OOB error, providing an internal evaluation of model quality without cross-validation.

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OOB Estimate

Unbiased estimate of the test error obtained by aggregating predictions on out-of-bag samples for each observation in the training set.

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Bagging Error

Generalization error of a bagging model, which can be efficiently estimated by the out-of-bag method without requiring an external validation set.

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Random Forest OOB

Specific application of out-of-bag error to random forests, where each tree is evaluated on samples not used in its bootstrap to estimate overall performance.

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OOB Variable Importance

Measure of variable importance calculated by evaluating the increase in OOB error when the values of a variable are randomly permuted in out-of-bag samples.

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OOB Cross-Validation

Alternative to traditional cross-validation using out-of-bag samples as internal validation sets for each bootstrap iteration.

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Bagging Variance Reduction

Fundamental property of bagging that reduces prediction variance by averaging the outputs of models trained on different bootstrap samples.

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OOB Confidence Interval

Confidence interval for the generalization error estimated from the distribution of out-of-bag errors across different bootstrap samples.

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Subagging OOB

Bagging variant using subsamples without replacement, where the out-of-bag estimation must be adapted to account for the different sampling strategy.

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OOB Proximity Matrix

Matrix measuring the proximity between observations based on the frequency where they fall into the same terminal leaves of trees evaluated on out-of-bag samples.

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Bagging Instability

Measure of the sensitivity of a base algorithm to variations in training data, a necessary condition for bagging and OOB estimation to be effective.

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OOB Learning Curve

Curve showing the evolution of the out-of-bag error as a function of the number of models in the ensemble, allowing optimization of the ensemble size without overfitting.

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