This was not an easy decision to make or taken lightly, but we felt this was the necessary action to take for the health, safety and well being of all our members. The decision was made in accordance with the guidelines set forth by Bishop Thomas J. We are considering other options to maintain community engagement and the fellowship portion of our ministry available to you during these unprecedented times. Stay tuned as we will be sharing more information on these future opportunities in the coming weeks. We are keeping each of you and your families in our prayers, please be safe and stay healthy. Together we remain United in Christ. See more.
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Speed dating is a relative new concept that allows researchers to study various theories related to mate selection. A problem with current research is that it focuses on finding general trends and relationships between the attributes. This report explores the use of machine learning techniques to predict whether an individual will want to meet his partner again after the 4-minute meeting based on their attributes that were known before they met.
It is shown that Random Forests perform better than Support Vector Machines and that extended attributes give better result for both classifiers. Furthermore, it is observed that the more information is known about the individuals, the better a classifier performs. Clubbing preferences of the partner stands out as an important attribute, followed by the same preference for the individual. The definition of short names is found in Appendix A.
This gives researchers the ability to study and confirm various theories related to mate selection. The problem with current research is that it focuses on finding general correlation between a set of attributes and the decision to prefer a partner. This is problematic because it does not consider the individual and his specific preferences. Thus, we want to investigate if we can develop a model that can predict whether a person will want to meet the partner again after their first 4-minute meeting based on data from one speed-dating experiment.
Our idea is to use machine learning techniques and a priori knowledge about the candidates.
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