Using a Predictive Rating System for Computer Programmers to Optimise Recruitment
0
Citation
26
Reference
10
Related Paper
Abstract:
Using a quantitative assessment system, the number of resumes reviewed to identify a suitable developer was reduced to 3.5% with a successful recruitment decision made in 10 working days of posting the job advertisement. This paper summarises the methodology for developing that rating system. The depth and quality of an available talent pool is a function of demand, which is demonstrated by comparing globally-scaled individual performance metrics. Public code repositories are accessed and the code quality assessed algorithmically. The performance score combines accuracy, timeliness and difficulty from a series of challenges. These three attributes form a meaningful predictive measure of performance by using a non-linear optimisation routine. Bootstrapping is used to validate the approach. This process randomly omitted a scored performance observation per coder in order to calculate the performance score from the retained scores. There was a strong relationship (r = 0.70) between the predicted 1-omitted-performance score with the actual omitted score highlighting the predictive power.Keywords:
Bootstrapping (finance)
Predictive power
Code (set theory)
Quality Score
Bootstrapping (finance)
Cite
Citations (76)
Bootstrapping (finance)
Baseline (sea)
Cite
Citations (0)
Abstract This paper aims at resolving a puzzle about the persuasiveness of bootstrapping. On the one hand, bootstrapping is not a persuasive method of settling questions about the reliability of a source. On the other hand, our beliefs that our sense apparatus is reliable is based on other empirically formed beliefs, that is, they are acquired via a presumably complex bootstrapping process. I will argue that when we doubt the reliability of a source, bootstrapping is not a persuasive method for coming to believe that the source is reliable. However, when being initially unaware of a source and its reliability, as in the case of forming beliefs about our sense apparatus, bootstrapping can be eventually persuasive.
Bootstrapping (finance)
Cite
Citations (5)
Bootstrapping (finance)
Sample (material)
Cite
Citations (14)
Bootstrapping (finance)
Sample (material)
Cite
Citations (49)
Knowledge acquisition is an iterative process. Most prior work used syntactic bootstrapping approaches, while semantic bootstrapping was proposed recently. Unlike syntactic bootstrapping, semantic bootstrapping bootstraps directly on knowledge rather than on syntactic patterns, that is, it uses existing knowledge to understand the text and acquire more knowledge. It has been shown that semantic bootstrapping can achieve superb precision while retaining good recall on extracting isA relation. Nonetheless, the working mechanism of semantc bootstrapping remains elusive. In this extended abstract, we present a theoretical analysis as well as an experimental study to provide deeper insights into semantic bootstrapping.
Bootstrapping (finance)
Cite
Citations (0)
Bootstrapping (finance)
Bootstrap model
Cite
Citations (212)
Abstract Carey rightly rejects the building blocks model of concept acquisition on the grounds that new primitive concepts can be learned via the process of bootstrapping. But new primitives can be learned by other acquisition processes that do not involve bootstrapping, and bootstrapping itself is not a unitary process. Nonetheless, the processes associated with bootstrapping provide important insights into conceptual change.
Bootstrapping (finance)
Cite
Citations (3)
A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.
Interpretability
Predictive power
Predictive modelling
Black box
Default
Cite
Citations (0)
There are various bootstrapping approaches depending on how bootstrap samples are selected. The conventional bootstrapping obtains random bootstrap samples by using all the units in the original sample. Balanced bootstrapping based on having individual observations with equal overall frequencies in all bootstrap samples and sufficient bootstrapping based on using only the distinct individual observations instead of all the units in the original sample are the two basic attempts proposed in this manner. This study compares the balanced, sufficient and conventional bootstrapping approaches in terms of efficiency, bootstrap confidence interval coverage accuracy, and average interval length. Although sufficient bootstrapping approach resulted in more efficient estimators and the narrower confidence intervals than the other two in all cases, none of the actual coverage level of confidence intervals was controlled within the desired limits. Conventional and balanced bootstrapping approaches have given quite similar results in terms of efficiency, coverage accuracy and average length.
Bootstrapping (finance)
Sample (material)
Coverage probability
Robust confidence intervals
Cite
Citations (0)