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Multivariate Analysis and Machine learning algorithms: basic concepts

Multivariate Analysis algorithms receive as input a set of discriminating variables. Each variable alone does not allow to reach an optimal discrimination power between two categories (signal and background). Therefore the algorithms compute an output that combines the input variables.

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 could be used as a variable on which a cut can be applied under a particular hypothesis test.

In particular, Machine Learning tools are models which have enough capacity to define their own internal representation of data to accomplish two main tasks : learning from data and make predictions without being explicitly programmed to do so.

How to execute it

Use Googe Colab 

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