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Stability: Difference between revisions
→Methods for Evaluating Stability
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#[[Cross-validation]]: This method is commonly used for evaluating model stability. In this approach, data is divided into multiple [[fold]]s and the model trained and assessed on each fold. The average performance across all folds then serves to judge how stable the model truly is. | #[[Cross-validation]]: This method is commonly used for evaluating model stability. In this approach, data is divided into multiple [[fold]]s and the model trained and assessed on each fold. The average performance across all folds then serves to judge how stable the model truly is. | ||
#[[Bootstrapping]]: This resampling technique involves drawing multiple examples with replacement from the training data to generate multiple [[training set | #[[Bootstrapping]]: This resampling technique involves drawing multiple examples with replacement from the training data to generate multiple [[training set]]s. The model is then trained on each set and its average performance used to assess its stability. | ||
#[[Regularization]]: This technique helps control [[overfitting]] in a model by adding a penalty term to the loss function. Regularization helps improve model stability by preventing it from fitting [[noise]] in data. | #[[Regularization]]: This technique helps control [[overfitting]] in a model by adding a penalty term to the loss function. Regularization helps improve model stability by preventing it from fitting [[noise]] in data. | ||