To little surprise, Canelo Alvarez (48-1-1, 34 KOs) enters his Saturday pay-per-view fight in Las Vegas with Julio Cesar Chavez Jr. (50-2-1, 32 KOs) as the clear favorite. Alvarez had previously entered the fight at -900 odds with Chavez the underdog at 550, according to a sportsbook in January. Alvarez, one of the best pound-for-pound boxers in the world, is coming off a technical knockout win over Liam Smith in September and a brutal knockout of Amir Khan in May 2016. But Alvarez's signature win is a unanimous decision over Miguel Cotto in November 2015. Cotto had entered the fight with three straight wins and nearly 15 years of experience.

Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline. The classification report visualizer displays the precision, recall, and F1 scores for the model.

WWE Payback 2017 certainly lived up to its name Sunday night, allowing several superstars to get retribution for recent losses. The eight matches resulted in two titles changing hands, and the main event likely set up a match for the No.1 championship on "Monday Night Raw." Braun Strowman ended the pay-per-view by defeating Roman Reigns, continuing to be the most dominant wrestler in all of WWE. He now has his eyes on the WWE Universal Championship, though Finn Balor announced his intention to reclaim the belt when he appeared on "Miz TV" as part of the Payback kickoff show. Let's take a look at the complete results of WWE Payback, including a recap and video for each match. Reigns stood little chance from the start.

The paper addresses the simultneous determination of goup-sparse loadings by block optimization, and the correlated problem of defining explained variance for a set of non orthogonal components. We give in both cases a comprehensive mathematical presentation of the problem, which leads to propose i) a new formulation/algorithm for group-sparse block PCA and ii) a framework for the definition of explained variance with the analysis of five definitions. The numerical results i) confirm the superiority of block optimization over deflation for the determination of group-sparse loadings, and the importance of group information when available, and ii) show that ranking of algorithms according to explained variance is essentially independant of the definition of explained variance. These results lead to propose a new optimal variance as the definition of choice for explained variance.

There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimization of an exponential likelihood function. These existing explanations, however, have been pointed out to be incomplete. A random forest is another popular ensemble method for which there is substantially less explanation in the literature. We introduce a novel perspective on AdaBoost and random forests that proposes that the two algorithms work for similar reasons. While both classifiers achieve similar predictive accuracy, random forests cannot be conceived as a direct optimization procedure. Rather, random forests is a self-averaging, interpolating algorithm which creates what we denote as a "spikey-smooth" classifier, and we view AdaBoost in the same light. We conjecture that both AdaBoost and random forests succeed because of this mechanism. We provide a number of examples and some theoretical justification to support this explanation. In the process, we question the conventional wisdom that suggests that boosting algorithms for classification require regularization or early stopping and should be limited to low complexity classes of learners, such as decision stumps. We conclude that boosting should be used like random forests: with large decision trees and without direct regularization or early stopping.

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions and turned out to be a well-known NP-hard problem and, hence, approximations are required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before. For this reason, in this work, we provide a detailed study of the different state-of-the-arts methods for structural learning on simulated data considering both BNs with discrete and continuous variables, and with different rates of noise in the data. In particular, we investigate the characteristics of different widespread scores proposed for the inference and the statistical pitfalls within them.

Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, we propose new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data. We apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.

In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs. This also includes a list of open ended questions that interviewers ask to get a feel of how often and how quickly you can think on your feet.There are some data analyst interview questions in this blog which can also be asked in a data science interview. These kind of analytics interview questions also measure if you were successful in applying data science techniques to real life problems. If you would like more information about Online Data Science course, please click the orange "Request Info" button on top of this page. Data Science is not an easy field to get into. This is something all data scientists will agree on. Apart from having a degree in mathematics/statistics or engineering, a data scientist also needs to go through intense training to develop all the skills required for this field. Apart from the degree/diploma and the training, it is important to prepare the right resume for a data science job, and to be well versed with the data science interview questions and answers. Consider our top 100 Data Science Interview Questions and Answers as a starting point for your data scientist interview preparation.