residuals of the training data vs residuals of the training sets

Both phrases are correct, but they are used in different contexts. 'Residuals of the training data' refers to the errors or the differences between the predicted values and the actual values in the training data. On the other hand, 'residuals of the training sets' would typically refer to the errors in multiple subsets of the training data if it has been divided into sets for some reason.

Last updated: March 17, 2024 • 539 views

residuals of the training data

This phrase is correct and commonly used in the context of machine learning and statistical analysis.

This phrase refers to the errors or the differences between the predicted values and the actual values in the training data.

Show examples from the web [+]

Jun 26, 2014 ... residuals of the training data (normal operation) processed by linear regression model. ϱ residuals of the testing data processed by linear.
Jun 8, 2014 ... ̺ residuals of the training data (faulty operation) processed by linear regres- sion model. ̺∗ residuals of the training data (normal operation) ...
Dec 4, 2000 ... dard deviation of the residuals of the training data points. In this case, the y-level reliability computes the degree of belief that the difference ...
this case, the bootstrap must be applied to the residuals of the training data rather than to the original data assuming that the residuals fulfill the iid-assumption.

Alternatives:

errors in the training data
deviations in the training data
discrepancies in the training data
discrepancies between predicted and actual values in the training data
training data residuals

residuals of the training sets

This phrase is correct and could be used when referring to errors in multiple subsets of the training data.

This phrase would typically refer to the errors in multiple subsets of the training data if it has been divided into sets for some reason.

Alternatives:

errors in the training sets
deviations in the training sets
discrepancies in the training sets
discrepancies between predicted and actual values in the training sets
training sets residuals

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residuals of the training data vs residuals of the training sets