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We can now start to define a the first architecture. The most simple approach is using fully connected layers (Dense layers in Keras/Tensorflow), with seluactivation function and a sigmoid final layer, since we are affording a binary classification problem.
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Scikit-learn is a simple and efficient tools tool for predictive data analysis, accessible to everybody, and reusable in various contexts. It is built on NumPy, SciPy, and matplotlib scientific libraries.
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Decision Trees and their extension Random Forests are robust and easy-to-interpret machine learning algorithms for Classification classification tasks.
Decision Trees comprise a simple and fast way of learning a function that maps data x to outputs y, where x can be a mix of categorical and numeric variables and y can be categorical for classification, or numeric for regression.
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Selection cuts can be tuned in order to achieve the best split level in each node according to some metrics (gini Gini index, cross-entropy...). Most of them are related to the purity of a node, that is the fraction of signal events over the whole events set in a given node P=S/(S+B).
The gain due to the splitting of a node A into the nodes B1 and B2, which depends on the chosen cut, is given by : ΔI=I(A)-I(B)-I(B2) , where I denotes the adopted metric (G or E, in case of the Gini index or cross-entropy introduced above). By varying the cut, the optimal gain may be achieved.
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