Failure resolved

-
Hi,

It seems that the failure has been resolved since yesterday "August 1st, 2017".

Again, sorry for the inconvenience. I hope that the continuity of service will be ensured throughout the summer.

Kind regards,

Ricco (August 2nd, 2017).

Comments

Post a Comment

Popular posts from this blog

iCloud Lock DNS Bypass Server DNS 78.109.17.60

-
Image
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 
Read more

Kohonen map with R

-
This tutorial complements the course material concerning the Kohonen map or Self-organizing map ( June 2017 ). In a first time, we try to highlight two important aspects of the approach: its ability to summarize the available information in a two-dimensional space; Its combination with a cluster analysis method for associating the topological representation (and the reading that one can do) to the interpretation of the groups obtained from the clustering algorithm. We use the R software and the “Kohonen” package (Wehrens et Buydens, 2007). In a second time, we carry out a comparative study of the quality of the partitioning with the one obtained with the K-means algorithm. We use an external evaluation i.e. we compare the clustering results with pre-established classes. This procedure is often used in research to evaluate the performance of clustering methods. It takes on its meaning when it is applied to artificial data where the true class membership is known. We use the K-Means and ...
Read more

Categorical predictors in logistic regression

-
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...
Read more