threshold : 0, // You can set threshold on how close to the edge ad should come before it is loaded. Default is 0 (when it is visible).
forceLoad : false, // Ad is loaded even if not visible. Default is false.
onLoad : false, // Callback function on call ad loading
onComplete : false, // Callback function when load is loaded
timeout : 1500, // Timeout ad load
debug : false, // For debug use : draw colors border depends on load status
xray : false // For debug use : display a complete page view with ad placements
console.log(“error loading lazyload_ad ” + exception);
Technical recruiter HackerRank has introduced Tech Talent Matrix, a subscription service that provides data to help with the hiring of software developers.
Tech Talent Matrix draws from an analysis of more than 150 million assessments and company data points. Recruiting performance is measured, with insights provided to help users hire talent. Enterprises are evaluated on their technical recruiting process, including the type of developers they can attract, how well candidates are being assessed, and the level of alignment between hiring managers and recruiters. Companies also can be benchmarked against peers based on industry and size. A company’s position on the matrix is a graphical representation of two factors:
- Candidate response score, which measures the performance of an organizations’ candidate outreach by tracking candidate journeys from the stage at which they are invited to take technical assessments.
- Assessment quality score, which measures the quality of a company’s assessments and how well assessments have been designed to evaluate candidates.
With Tech Talent Matrix Matrix, companies are given insights on how to improve their scores. For the candidate response score, advice is provided on strengthening their tech talent brand. For a low assessment-quality score, guidance is provided on ensuring technical evaluations are relevant for roles.
With Tech Talent Matrix, HackerRank modeled candidate feedback using various regression models to understand what a good feedback score would be. Clustering techniques were applied to cluster similar distributions, followed by the use of machine learning techniques such as the XGBoost library for classifying distributions.
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