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YZ Sözlüğü

Yapay Zekanın tam sözlüğü

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kategoriler
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alt kategoriler
23.060
terimler
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terimler

Balanced Random Forest

A variant of Random Forest designed to handle imbalanced datasets by creating decision trees on bootstrap subsamples where each class is equally represented.

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Balanced Bootstrap

A sampling technique where, for each iteration, a bootstrap sample is drawn to guarantee equal representation of classes, often by undersampling the majority class or oversampling the minority class.

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Majority Class Undersampling

A method to reduce class imbalance by randomly removing observations from the majority class to reduce its predominance in the training dataset.

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Minority Class Oversampling

A technique aimed at increasing the number of observations of the minority class, either by duplication or by generating new synthetic observations, to balance the class distribution.

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Bootstrap Sample

A random sample drawn with replacement from the original dataset, used in bagging methods to train each model in the ensemble on a slightly different subset of the data.

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Gini Score

A measure of node impurity in a decision tree, quantifying the probability that a randomly chosen observation from the node would be misclassified if it were randomly labeled according to the class distribution.

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AUC-ROC (Area Under the Receiver Operating Characteristic Curve)

A performance metric that measures a classifier's ability to distinguish between classes, representing the area under the curve that plots the true positive rate against the false positive rate.

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EasyEnsemble

An ensemble learning algorithm that creates multiple subsets of the majority class, trains a classifier on each subset combined with the entire minority class, and aggregates the predictions.

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BalanceCascade

Iterative ensemble method that sequentially trains classifiers on increasingly balanced datasets, removing correctly classified examples from the majority class at each step.

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Recall (Sensitivity)

Metric that measures the proportion of actual positive observations that were correctly identified by the model, essential for evaluating performance on the minority class.

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