The Future Is Here: Lilium Jet's Electric Flying Car Completes First Test Flights

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Flying cars are a cornerstone of futurism but most models have been consigned to the dustbin of history.
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Cost-Sensitive Learning (slides)

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This course material presents approaches for the consideration of misclassification costs in supervised learning. The baseline method is the one for which we do not take into account the costs. Two issues are studied : the metric used for the evaluation of the classifier when a misclassification cost matrix is provided i.e. the expected cost of misclassification (ECM); some approaches which enable to guide the machine learning algorithm towards the minimization of the ECM. Keywords : cost matrix, misclassification, expected cost of misclassification, bagging, metacost, multicost Slides : Cost Sensitive Learning References : Tanagra Tutorial, " Cost-senstive learning - Comparison of tools ", March 2009. Tanagra Tutorial, " Cost-sensitive decision tree ", November 2008.
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iCloud Lock DNS Bypass Server DNS 78.109.17.60

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DNS 78.109.17.60 Watch video, take pictures, record videos, listen music and radio on your iCloud Locked device while you are waiting full bypass You can try  Applications => Crash  method to unlock your device, but only few people unlocked. Server created by one developer from Ukraine. DNS server and HTTP server fully coded on C++ And thats why my server is failover. More than 12K devices online. More than 100K menu pages requesting per day. And it uses only 2% CPU load. Download tools 
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Categorical predictors in logistic regression

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The aim of the logistic regression is to build a model for predicting a binary target attribute from a set of explanatory variables (predictors, independent variables), which are numeric or categorical. They are treated as such when they are numeric. We must recode them when they are categorical. The dummy coding is undeniably the most popular approach in this context. The situation becomes more complicated when we perform a feature selection . The idea is to determine the predictors that contribute significantly to the explanation of the target attribute. There is no problem when we consider a numeric variable. It is either excluded or either kept in the model. But how to proceed when we handle a categorical explanatory variable? Should we treat the dichotomous variables associated to a categorical predictor as a whole that we must exclude or include into the model? Or should we treat the each dichotomous variable independently? How to interpret the coefficients of the selected dichot...
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