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Stability: Difference between revisions

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(Created page with "==Introduction== Stability in machine learning refers to the robustness and dependability of a model's performance when exposed to small variations in training data, hyperparameters, or even the underlying data distribution. This is an essential aspect to consider when building models for real-world applications since even small changes can drastically impact predictions made by the model. ==Types of Stability== In machine learning, there are...")
 
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{{see also|Machine learning terms|Stability AI}}
==Introduction==
==Introduction==
[[Stability]] in [[machine learning]] refers to the robustness and dependability of a [[model]]'s performance when exposed to small [[variation]]s in [[training data]], [[hyperparameter]]s, or even the underlying [[data distribution]]. This is an essential aspect to consider when building models for real-world applications since even small changes can drastically impact predictions made by the model.
[[Stability]] in [[machine learning]] refers to the robustness and dependability of a [[model]]'s performance when exposed to small [[variation]]s in [[training data]], [[hyperparameter]]s, or even the underlying [[data distribution]]. This is an essential aspect to consider when building models for real-world applications since even small changes can drastically impact predictions made by the model.
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In machine learning, there are various methods available for assessing stability. Examples include:
In machine learning, there are various methods available for assessing stability. Examples include:


#Cross-validation: This method is commonly used for evaluating model stability. In this approach, data is divided into multiple folds 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 samples with replacement from the training data to generate multiple training sets. The model is then trained on each set and its average performance used to assess its stability.
#[[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.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
indicates a model's capacity to remain accurate even when small changes are made to its training data or construction. Think of it like building a tower with blocks; if it is constructed poorly, even minor winds could bring it down; on the other hand, if built robustly and securely, even strong winds won't knock it over. We can make our tower stronger using techniques like cross-validation, bootstrapping, and regularization.
indicates a model's capacity to remain accurate even when small changes are made to its training data or construction. Think of it like building a tower with blocks; if it is constructed poorly, even minor winds could bring it down; on the other hand, if built robustly and securely, even strong winds won't knock it over. We can make our tower stronger using techniques like cross-validation, bootstrapping, and regularization.
[[Category:Terms]] [[Category:Machine learning terms]]