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- You will learn how a multivariate analysis algorithm works (see the below introduction) and more specifically how a Machine Learning model must be implemented;
- you will acquire basic knowledge about the Higgs boson physics as described by the Standard Model. During the exercise you will be invited to plot some physical quantities in order to understand what is the underlying Particle Physics problem;
- you will be invited to change hyperparameters of the ANN and the RF algorithms to understand better what are the consequences in terms of the models' performances;
- you will understand that the choice of the *input variables* is the key to the goodness of the algorithm since an optimal choice allows achieving the best possible performances;
- moreover, you will have the possibility of changing the background datasets, the decay channels of the final state particles, and seeing how the ML algorithms' performance changes.
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This is what every Multivariate Analysis (MVA) discriminator does. The discriminant output, also called discriminator, score, or classifier, is used as a test statistic and is then adopted to perform the signal selection. It It could be used as a variable on which we decide to cut in a a cut can be applied under a particular hypothesis test.
In particular, Machine Learning tools are models that have enough capacity to define their own internal representation of the data to accomplish a task: learning from data and make predictions without being explicitly programmed to do so.
In the case of binary classification, firstly the algorithm is trained with two datasets:
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