Menu
The new European data protection law requires us to inform you of the following before you use our website:We use cookies and other technologies to customize your experience, perform analytics and deliver personalized advertising on our sites, apps and newsletters and across the Internet based on your interests. By clicking “I agree” below, you consent to the use by us and our third-party partners of cookies and data gathered from your use of our platforms. See our and to learn more about the use of data and your rights. You also agree to our.
CERN's main focus is particle physics – the study of the fundamental constituents of matter – but the physics programme at the laboratory is much broader, ranging from nuclear to high-energy physics, from studies of antimatter to the possible effects of cosmic rays on clouds.Since the 1970s, particle physicists have described the fundamental structure of matter using an elegant series of equations called the. The model describes how everything that they observe in the universe is made from a few basic blocks called fundamental particles, governed by four forces. Physicists at CERN use the world's most powerful particle accelerators and detectors to test the predictions and limits of the Standard Model.
Over the years it has explained many experimental results and precisely predicted a range of phenomena, such that today it is considered a well-tested physics theory.But the model only describes the 4% of the known universe, and questions remain. Will we see a unification of forces at the high energies of the (LHC)? Why is gravity so weak?
Why is there more matter than antimatter in the universe? Is there more exotic physics waiting to be discovered at higher energies? Will we discover evidence for a theory called at the LHC? Or understand that gives particles mass? Scientists at CERN are trying to find out what the smallest building blocks of matter are.All matter except dark matter is made of molecules, which are themselves made of atoms.
Inside the atoms, there are electrons spinning around the nucleus. The nucleus itself is generally made of protons and neutrons but even these are composite objects. Inside the protons and neutrons, we find the quarks, but these appear to be indivisible, just like the electrons.Quarks and electrons are some of the elementary particles we study at CERN and in other laboratories. But physicists have found more of these elementary particles in various experiments, so many in fact that researchers needed to organize them, just like Mendeleev did with his periodic table.This is summarized in a concise theoretical model called the. Today, we have a very good idea of what matter is made of, how it all holds together and how these particles interact with each other.
Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with a specific model and data set) are expected to be accurate.Confidence Interval. Confidence intervals are used to assess how reliable a statistical estimate is. Wide confidence intervals mean that your model is poor (and it is worth investigating other models), or that your data is very noisy if confidence intervals don't improve by changing the model (that is, testing a different theoretical statistical distribution for your observations.) Modern confidence intervals are model-free, data -driven: to see how to compute them. A more general framework to assess and reduce sources of variance is called analysis of variance. Modern definitions of variance.Confusion Matrix.
Physics Model Projects
Used in the context of clustering. These N x N matrices (where N is the number of clusters) are designed as followed: the element in cell (i, j) represents the number of observations, in the test training set (as opposed to the control training set, in a cross-validation setting) that belong to cluster i and are assigned (by the clustering algorithm) to cluster j. When these numbers are transformed into proportions, these matrices are sometimes called contingency tables. A wrongly assigned observation is called false positive (non-fraudulent transaction erroneously labelled as fraudulent) or false negative (fraudulent transaction erroneously labelled as non- fraudulent). The higher the concentration of observations in the diagonal of the confusion matrix, the higher the accuracy / predictive power of your clustering algorithm.Gain and Lift Chart. Lift is a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model.
Cumulative gains and lift charts are visual aids for measuring model performance. Both charts consist of a lift curve and a baseline.Kolmogorov-Smirnov Chart. This non-parametric statistical test is used to compare two distributions, to assess how close they are to each other. In this context, one of the distributions is the theoretical distribution that the observations are supposed to follow (usually a continuous distribution with one or two parameters, such as Gaussian law), while the other distribution is the actual, empirical, parameter-free, discrete distribution computed on the observations.Chi Square. It is another statistical test similar to Kolmogorov-Smirnov, but in this case it is a parametric test. It requires you to aggreate observations in a number of buckets or bins, each with at least 10 observations.ROC curve. Unlike the lift chart, the ROC curve is almost independent of the response rate.
The receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity or the sensitivity index d', known as 'd-prime' in signal detection and biomedical informatics, or recall in machine learning. The false-positive rate is also known as the fall-out and can be calculated as (1 - specificity). The ROC curve is thus the sensitivity as a function of fall-out.Gini Coefficient.
The Gini coefficient is sometimes used in classification problems. Gini = 2.AUC - 1, where AUC is the area under the curve (see the ROC curve entry above). A Gini ratio above 60% corresponds to a good model.
Not to be confused with the or Gini impurity, used when building decision trees.Root Mean Square Error. RMSE is the must used and abused metric to compute goodness of fit. It is defined as the square root of the absolute value of the correlation coefficient between true values and predicted values, and widely used by Excel users.L^1 version of RSME. The RSME metric (see above entry) is an L^2 metric, sensitive to outliers.
Modern metrics are L^1 and sometimes based on rank statistics rather than raw data. One of these new metrics, developed by our data scientist,.Cross Validation. This is a general framework to assess how a model will perform in the future; it is also used for model selection. It consists of splitting your training set into test and control data sets, training your algorithm (classifier, or predictive algorithm) on the control data set, and testing it on the test data set. Since the true values are known on the test data set, you can compare them with your predicted values, using one of the other comparison tools mentioned in this article. Thirst mod 1.12.2. Usually the test data set itself is split into multiple subsets or data bins, to compute confidence intervals for predicted values. The test data set must be carefully selected, and must include different time frames and different types of observations (compared with the control data set), each with enough data points, in order to get sound, reliable conclusions as how the model will perform on future data, or on data that has slightly involved.
Another idea is to introduce noise in the test data set and see how it impacts prediction: this is referred to as model sensitivity analysis.Predictive Power. This metric was developed internally at Data Science Central by our data scientist. It is related to the concept of entropy or the Gini index mentioned above in this article. It was designed as a satisfying interesting properties, and used to select a good subset of features in any machine learning project, or as a criterion to decide which node to split at each iteration, when building decision trees.DSC Resources. Career:. Knowledge:. Buzz:.
Misc: Additional Reading.Follow us on Twitter:.