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When precision is high and recall is low?
The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
What does cross-validation score tell us?
Cross-validation is a statistical method used to estimate the skill of machine learning models. That k-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset.
What is high precision but low accuracy?
In a laboratory situation, high precision with low accuracy often results from a systematic error. Either the measurer makes the same mistake repeatedly or the measuring tool is somehow flawed. A poorly calibrated balance may give the same mass reading every time, but it will be far from the true mass of the object.
Is it possible to have 1 cross validated accuracy?
Here is the code: First of all, you should use cross_val_predict to get you predictions vector, so that you followed approximately the same validation scheme to get them : Then, it is totally possible to have 1 for recall or precision given that these are the scores for the considered positive class for scikit.
Which is better high precision or low recall?
Furthermore, the precision of H, i.e. the percentage of retrieved documents are relevant, is high too, so that’s even better in case having irrelevant document amongst the retrieved documents is costly.
What is low recall for unbalanced dataset?
When predicting I get a low precision (0.47) for the minority class in the validation set; recall is 0.88. I tried to use several oversampling and under-sampling methods (performed on the training set) which did not improve the precision since the validation set is unbalanced as well to reflect the real class distribution.
Is the recall on a classifier low or high?
It is a terrible classifier if you try to retrieve as many documents of type R as possible, because the recall on R is 0.23 only, which means you are going to miss 77% of the documents.