Test loss: Difference between revisions

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 {{see also|Machine learning terms}}
 ==Introduction==
-Machine learning algorithms measure their model's ability to make accurate predictions on unseen data. The test loss provides an assessment of a model's generalization ability, or its capacity for making accurate predictions when presented with new, unseen information that was not seen during training.
+[[Test loss]] is a [[metric]] that measures a [[model]]'s [[loss]] against the [[test data set]]. Note that the [[test dataset]] is a separate [[dataset]] from the [[training data set]] and the [[validation data set]]. Testing the model on the test set is like a final test for an already trained [[machine learning model]]. The lower the test loss is, the better the model is.
-The test loss is calculated by comparing the model's predictions on test data with actual values for target variables. This difference, known as an "error" or "residual", serves to measure how accurately predictions made on the test data reflect actual outcomes. It serves to reflect how well-fitted the model's predictions were to the actual data.
+[[Machine learning]] [[algorithm]]s measure their model's ability to make accurate predictions on unseen [[data]]. The test loss provides an assessment of a model's generalization ability, or its capacity for making accurate predictions when presented with new information that was not seen during [[training]].
-Calculating a test loss requires consideration of the particular problem being addressed and desired properties of the model. Common loss functions include mean squared error, mean absolute error, and categorical cross-entropy.
+The test loss is calculated by comparing the model's predictions on [[test data]] with actual values for target variables ([[labels]]). This difference, known as an [[error]], serves to measure how accurately predictions made on the test data reflect actual outcomes. It serves to reflect how [[well-fitted]] the model's predictions were to the actual data.
+We want to minimize the test loss. A large test loss vs. [[training loss]] or [[validation loss]] might indicate that we are [[overfitting]] the model and might need to use [[regularization]].
+Calculating a test loss requires consideration of the particular problem being addressed and desired properties of the model. Common [[loss function]]s include [[mean squared error]], [[mean absolute error]], and [[categorical cross-entropy]].
 ==Mean Squared Error==
-Mean squared error (MSE) is a commonly used measure when attempting to predict an ongoing target variable. MSE is calculated as the average of squares between predicted values and actual values, or MSE for short.
+[[Mean squared error]] (MSE) is a commonly used measure when attempting to predict an ongoing target variable. MSE is calculated as the average of squares between predicted values and actual values (labels).
-MSE is a smooth and differentiable function, making it suitable for optimization algorithms such as gradient descent. Furthermore, MSE has the advantageous property of being sensitive to large errors; this means a model with an increased MSE is likely to make major mistakes on some instances in its test set.
+MSE is a smooth and differentiable function, making it suitable for [[optimization algorithm]]s such as [[gradient descent]]. Furthermore, MSE has the advantageous property of being sensitive to large errors; this means a model with an increased MSE is likely to make major mistakes in some instances in its test set.
 ==Mean Absolute Error==
-Mean absolute error (MAE) is a commonly used measure for regression problems. MAE is calculated as the average of all residual values between predicted values and actual values.
+[[Mean absolute error]] (MAE) is a commonly used measure for regression problems. MAE is calculated as the average of all residual values between predicted values and actual values.
 MAE is a robust loss function that is insensitive to outliers, making it ideal for problems where there may be some instances in the test set with large errors. Unlike MSE, however, MAE is nondifferentiable which may make optimizing with gradient-based algorithms more challenging.
 ==Categorical Cross-Entropy==
-Categorical cross-entropy is a widely used approach in classification problems, where the aim is to accurately predict a categorical target variable. Categorical cross-entropy is calculated as the average of negative log likelihoods associated with predicted class probabilities.
+[[Categorical cross-entropy]] is a widely used approach in [[classification]] problems, where the aim is to accurately predict a categorical target variable. Categorical cross-entropy is calculated as the average of negative log likelihoods associated with predicted class probabilities.
 Categorical cross-entropy is a smooth and differentiable function with the desirable property of assigning a large loss to predictions with low confidence. This property makes categorical cross-entropy ideal for classification problems where it's necessary to penalize models for making incorrect predictions with high assurance.
 ==Explain Like I'm 5 (ELI5)==
-The test loss is an indicator of how well a machine learning model can predict unknown events. It compares what the model thinks will occur with what actually does, and there are various methods for calculation depending on the problem at hand. If there are many mistakes made by the model, its test loss will be high.
+Test loss is like a test we give our model to see how well it understands what we taught it. Just like taking an exam in school to demonstrate your mastery of content, test loss helps us determine just how well our model comprehends what was presented to it.
-==Explain Like I'm 5 (ELI5)==
-Sure! Imagine you have a basket of apples and want to guess how many there are inside. While you can count them out yourself, sometimes your guess may not match up exactly with what the actual number of apples is. In such cases, it's wise to count twice and double up on guesses for safety's sake.
-Similar to human brains, machine learning models also make errors when they attempt to guess the correct answer for something. When learning, the model looks at examples and attempts to guess the correct response; the difference between its guess and actual answer is known as a "loss," which serves to show how wrong its guess was.
+When teaching a model, we provide it with examples to learn from and also keep some unknown so we can test its knowledge later on. Test loss is an indicator of how well the model has learned what we taught it; the lower this number, the better equipped it will be to guess answers when faced with questions that have never been asked before.
-Machine learning seeks to minimize loss so the model can make accurate guesses. This is similar to trying to estimate how many apples are in a basket as close as possible to its actual number.
+Just like when you receive a high grade on a test, a low test loss indicates that our model is doing an effective job of understanding what we have taught it. Conversely, a high loss indicates our model is struggling with understanding, much like when receiving an unsatisfactory grade on your exam.
-[[Category:Terms]] [[Category:Machine learning terms]]
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

Test loss: Difference between revisions

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Revision as of 20:47, 17 March 2023