Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Stability: Difference between revisions
no edit summary
(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...") |
No edit summary |
||
| Line 1: | Line 1: | ||
{{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. | ||
| Line 12: | Line 13: | ||
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 | #[[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 | #[[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]] | |||