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The loss function and the accuracy metrics gives give us a measure of the overtraining (overfitting) of the ML algorithm. Over-fitting happens when a ML algorithm learns to recognize a pattern that is primarily based on the training (validation) sample and that is nonexistent when looking at the testing (training) set (see the plot on the right side to understand what we would expect when overfitting happens).
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- Artificial Neural Network rates fixing an ANN score threshold from dataframedata frame
- Exercise 2 - Random Forest rates fixing a RF score threshold from dataframedata frame
- Plot some physical quantities after that the event selection is applied
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Artificial Neural Network rates fixing an ANN score threshold from dataframedata frame
Let's fix a cut (looking at the performance of our models in terms of the previous purity*efficiency metrics plot) on our test statistic (ANN score and RF score) to select mostly VBF Higgs production signal events!
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