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When creating a score for a name match, you may want to adjust it according to the experience, education and skills of both players. This can include both individual and overall score calculations.

## Calculating the overall match score

There are various ways to calculate the overall match score. For instance, the value can be determined based on the word vector similarity score, the parsed word score, the overall match weight, or any other number of factors. However, the match score is the most important factor in identifying the relationship between textual statements. The more scores used, the more important the overall match score.

The overall match score is calculated by the processor of claim 22. This may use a number of different rules and tables. It may also include a term extractor, a string matcher, a lexicon matcher, a semantic matcher, or a combination of these. In addition, the processor can use Word Frequency Statistics to adjust the match score.

The threshold value of the overall match score is set according to the importance of the field and the uncertainty percentage of the match. This can be determined from the graph in Fig. 8, and can be helpful in deciding the value of the threshold.

## Calculating the match scores for each individual component of Experience, Education, and Skills

The calculation of the match scores for each individual component of Experience, Education, and Skills is a bit more complex than it sounds. However, the results can be distilled into a few categories: experience, education, and skills. Some of the more important components may be lumped together. For example, the amount of education may be determined by the total number of years the employee has been in the workforce, but the level of skill in a particular skill might be a function of years of training rather than actual years worked. This might make the matching algorithm a bit less exact than a sane person would prefer.

The best way to go about calculating match scores is to use a statistical sampling methodology. For example, a household sample survey is probably more reliable than an employer-sponsored study. A random sample of employees is also a safer bet, as is an analysis of a subset of the population.

## Adjusting the score of a name match

As with many things in life, there is a right way and a wrong way to do it. In the case of adjusting the score of a name match, it isn’t as simple as just updating a single attribute. For instance, in the example in the table above, you would have to alter the weighting of the attributes to find the lowest score. By modifying the same attribute, you would be able to achieve a matching name with an average score of 40. If you’re a savvy data miner, you may even be able to obtain a score of a mere 15! To be on the safe side, avoid the temptation of tinkering with the numbers. The last thing you need is a name match tainting your score of the winning party!

## Adjusting the score of a cognitive match

There are several ways to adjust the score of a cognitive match. You can use a PI cognitive assessment conversion table to convert scores into percentiles. You can also filter the matches by using the Favorites feature. These features will help you pass on candidates who don’t fit your needs.

In addition, if you are using a Cognitive Job Target, you can also adjust the score of a cognitive match. The Job Target, which is displayed in the Assessments section of the Job Details page, is calculated as 50% of the Cognitive Assessment scores and 50% of the Candidate Behavioral Assessment scores. If the candidate’s scores do not fall within these ranges, they won’t be included in the top matches.

For example, if a candidate has a raw score of 82, but a Cognitive Job Target of 83, the software will generate a matching score of 1 on the scale of 1-10. This score will show up in the Job Matching feature. Similarly, if the candidate has a scale score of 70, but a Cognitive Job Target of 75, the matching score will be a 7.

## Recording the final match score

When you are a Scorekeeper Referee in a match, you must record the final score of the game. This is done to ensure that both teams have a fair chance at winning the match. The score sheet you use can be either a paper or digital version. If you choose to use a paper scoresheet, you will need to write down the match number on the top of the score sheet. You will also need to have the team numbers on the blank lines in the bottom third of the scoresheet.

The most effective way to ensure that you record the most appropriate and relevant information is to check with the other teams in the match. They are the ones who will know if you have missed a scoring object. It is also important to keep in mind that you may not have all of the time to record every single object. A missing object can be detrimental to a team’s ranking, so be sure to keep track of the things that you can.