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Refers to general procedures that attempt to determine the generalizability of a statistical result. Cross-validation arises frequently in the context of assessing how a particular model fit predicts future observations. Methods for cross-validation usually involve withholding a random subset of the data during model fitting and quantifying how accurate the withheld data are predicted and repeating this process to get a measure of prediction accuracy.

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What is GridSearchCV doing after it finishes evaluating the performance of parameter combina...

Yep I figured it out. The answer is that by default GridSearchCV's last act is to expose the API of the estimator object you passed so that you can directly call things like .predict() or .score() on …
Dan Scally's user avatar
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6 votes
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Interpretability of RMSE and R squared scores on cross validation

And one last thing, is my result on X_train indicative that my features are informative enough to learn the target? or is the R² train score somehow biased? High scoring fits on training data doe …
Dan Scally's user avatar
  • 1,784