Such a structure is also called Feedforward Multilayer Perceptron (MLP, see the picture).
The output of the node kth node of the layers nth layers is computed as the weighted average of the input variables, with weights that are subject to optimization via training.
Then a bias or threshold parameter is w0 is applied. This bias accounts for the random noise, in the sense that it measures how well the model fits the training set (i.e. how much the model is able to correctly predict the known outputs of the training examples.) The output of a given node is: .
During training we optimize the loss function, i.e. reduce the error between actual and predicted values. Since we deal with a binary classification problem, the can ytrue can take on just two values, ytrue = 0 (for hypothesis H1) and = 1 (for hypothesis H0).
A popular algorithm to optimize the weights consists of iteratively modifying the weights after each training observation or after a bunch of training observations by doing a minimization of the loss function.
Question to students: What happens if you switch to the decay 4e decay channel? You can submit your model (see the ML challenge below) for this physical process as well!
- You can participate as a single participant or as a team
- The winner is the one scoring the best AUC in the challenge samples!
- In the next box, you will find some lines of code for preparing an output csv file, containing your y_predic for this new dataset!
- Choose a meaningful name for your result csv file (i.e. your name, or your team name, the model used for the training phase, and the decay channel - 4 or 4 4μ or 4e - but avoid to submit
- Download the csv file and upload it here: https://recascloud.ba.infn.it/index.php/s/CnoZuNrlr3x7uPI
- You can submit multiple results, paying attention to name them accordingly (add the version number, such as
- You can use this exercise as a starting point (train over constituents)
- We will consider your best result for the final score.
- The winner will be asked to present the ML architecture!