> For the complete documentation index, see [llms.txt](https://lauradang.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lauradang.gitbook.io/notes/machine-learning/scikit-learn/measuring-model-accuracy.md).

# Measuring Model Accuracy

A. predictor.score(X,Y) internally calculates Y'=predictor.predict(X) and then compares Y' against Y to give an accuracy measure. This applies not only to logistic regression but to any other model.

B. logreg.score(X\_train,Y\_train) is measuring the accuracy of the model against the training data. (How well the model explains the data it was trained with). <-- But note that this has nothing to do with test data.

C. logreg.score(X\_test, Y\_test) is equivalent to your print(classification\_report(Y\_test, Y\_pred)). But you do not need to calculate Y\_pred; that is done internally by the library