residuals of the training vs residuals of the validation

Both phrases are correct, but they are used in different contexts. 'Residuals of the training' refers to the errors or the differences between the predicted values and the actual values in the training dataset. On the other hand, 'residuals of the validation' refers to the errors or differences in the validation dataset. It's important to use the correct term depending on whether you are analyzing the training or validation phase of a model.

Last updated: March 17, 2024 • 676 views

This phrase is correct and commonly used in the context of analyzing errors in a training dataset.

"residuals of the training"

This phrase refers to the errors or differences between the predicted values and the actual values in the training dataset of a model. It is used in the context of evaluating the performance of a model during the training phase.

Examples:

The residuals of the training data indicate how well the model fits the training dataset.

Show examples from the web [+]

One method of estimating bias is by bootstrapping the residuals of the training points. One produces a "bootstrap sample" of the learner's residuals on the ...
One method of estimating bias is by bootstrapping the residuals of the training. points. One produces a \bootstrap sample" of the learner's residuals on the ...
Sep 14, 1995 ... ping the residuals of the training points. Based on the m available training points, and the predictor's t to these points, a \bootstrap sample" is ...
Sep 20, 2015 ... mean difference between the residuals of the training set compared to the testing set. A table would help to illustrate this. Furthermore, it is ...

Alternatives:

training residuals
errors in the training data
training dataset residuals
training errors

This phrase is correct and commonly used in the context of analyzing errors in a validation dataset.

"residuals of the validation"

This phrase refers to the errors or differences between the predicted values and the actual values in the validation dataset of a model. It is used in the context of evaluating the generalization performance of a model on unseen data.

Examples:

The residuals of the validation set help assess how well the model generalizes to new data.

Show examples from the web [+]

The SFR is the sum of squared residuals of the validation spectrum relative to the average sum of squared residuals of the calibration set. The spectral residual ...
adding biases to the validation sample ages and then analyzing the sum of squared residuals of the validation samples'. ∆14C about the LOESS-smoothed ...
The SFR is the sum of squared residuals of the validation spectrum relative to the average sum of squared residuals of the calibration set. The spectral residual ...
Oct 28, 2013 ... Model residuals of the validation datasets. Long-term data. Residuals (observed minus predicted onset date) differed between cities (Figure 7a ...

Alternatives:

validation residuals
errors in the validation data
validation dataset residuals
validation errors

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residuals of the training vs residuals of the validation