For the first time in more than three months, a "SmackDown Live" pay-per-view is on the schedule. WWE Backlash 2017 is set for Sunday night in Rosemont, Illinois at the Allstate Arena with a few new faces set to compete in some of the card's biggest matches. Below are WWE Backlash predictions for every match on the card. Eight matches are scheduled, and three championships will be on the line. It was pretty surprising when Mahal became the No.1 contender for the top belt on "SmackDown Live," and it would be even more shocking to see him win the title.

Building a quality machine learning model for text classification can be a challenging process. You need to build a training dataset, test different parameters for your model, fix the confusions, among other things. On this post, we will describe the process on how you can successfully train text classifiers with machine learning using MonkeyLearn. What are the categories or tags that you want to assign to your texts? This is the first question you need to answer when you start working on your text classifier.

Receiver operating characteristic or ROC curves are of great interest in evaluating many security systems such as biometric authentication. They visualize the trade-off between the number of security breaches and the level of convenience. In the earlier work, ROC curves and their decision boundaries were studied for various classifiers. Here, further studies are conducted to identify problems of ROC curve analysis when artificial neural network (ANN) classifiers' net values are used. Graphical decision boundaries and experimental results on the IRIS biometric authentication system reveal the over-fitting in the ROC curve analysis. This graphical decision boundaries suggest that ANN classifiers with two output units are more desirable than those with a single output unit for two class classification problems.

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance.

In this report, an automated bartender system was developed for making orders in a bar using hand gestures. The gesture recognition of the system was developed using Machine Learning techniques, where the model was trained to classify gestures using collected data. The final model used in the system reached an average accuracy of 95%. The system raised ethical concerns both in terms of user interaction and having such a system in a real world scenario, but it could initially work as a complement to a real bartender.

The number of trees T in the random forest (RF) algorithm for supervised learning has to be set by the user. It is controversial whether T should simply be set to the largest computationally manageable value or whether a smaller T may in some cases be better. While the principle underlying bagging is that "more trees are better", in practice the classification error rate sometimes reaches a minimum before increasing again for increasing number of trees. The goal of this paper is four-fold: (i) providing theoretical results showing that the expected error rate may be a non-monotonous function of the number of trees and explaining under which circumstances this happens; (ii) providing theoretical results showing that such non-monotonous patterns cannot be observed for other performance measures such as the Brier score and the logarithmic loss (for classification) and the mean squared error (for regression); (iii) illustrating the extent of the problem through an application to a large number (n = 306) of datasets from the public database OpenML; (iv) finally arguing in favor of setting it to a computationally feasible large number, depending on convergence properties of the desired performance measure.

Unlike evaluating the accuracy of models that predict a continuous or discrete dependent variable like Linear Regression models, evaluating the accuracy of a classification model could be more complex and time-consuming. Before measuring the accuracy of classification models, an analyst would first measure its robustness with the help of metrics such as AIC-BIC, AUC-ROC, AUC- PR, Kolmogorov-Smirnov chart, etc. The next logical step is to measure its accuracy. To understand the complexity behind measuring the accuracy, we need to know few basic concepts. E.g. – A classification model like Logistic Regression will output a probability number between 0 and 1 instead of the desired output of actual target variable like Yes/No, etc.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts, and others provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers' behavior can be easily analyzed and the risks around loan can be reduced.

For fraudulent claims, that would be a binary indicator for known fraudulent claims (no false positives) and known legitimate claims. Check to see if you have this data set and the time period for the data. You will need a representative sample of both types of claims. For a "suspicious claims" model, the dependent variable would be defined a little differently. You need a set of claims that were flagged as suspicious, investigated and found to be fraudulent.

Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.