Domain experts without formal training in mathematics or computer science can often find statistical explanations as hard to interpret. First of all, explanations are often displayed as complex plots without instructions. Plots are using different scales mixing probabilities with relative changes in model’s prediction. Packages from DrWhy.AI toolbox generate lots of graphical explanations for predictive models.
In fine, they explain how the model’s prediction would change if we perturb the instance being explained. For example, ceteris_paribus() explanation explores how would the prediction change if we change the values of a single feature while keeping the https://en.wikipedia.org/wiki/Debits_and_credits other features unchanged. describe(break_down_explanation) # Random Forest predicts, that the prediction for the selected instance is 0.639 which is higher than the average. # The most important variables that increase the prediction are gender, fare.
The objective is that by using such simplified costs, Member States can considerably reduce the administrative burden and errors associated with implementing the ESF. This study analyses the specific contribution from the ESF to the promotion of employment opportunities, education and social inclusion in Germany, based on the experience in the programming period. This synthesis report focuses on the support https://www.youtube.com/results?search_query=metatrader+4 provided by ESF to women and young people. The main resource used in creating the synthesis report is a set of 10 detailed country reports, 5 focussed on women (DE, ES, GR, PL and UK) and 5 on young people (AT, CZ, FR, IT and PT). In preparation of the ESF multi-annual framework, the European Commission organised a learning seminar on setting and adjusting targets for ESF Operational Programmes.
To account for expenditures, indicators on outputs (numbers of participants, entities) and results (numbers of participants finding employment, gaining a qualification etc.) are essential. This background paper discusses methodologies for setting and adjusting quantified cumulative employment targets. This Report provides the third annual overview of the implementation of the more than 530 shared management (national and regional) programmes based on the annual programme reports received in mid-2018. Specifically, it summarises available performance information covering implementation in the years 2014 to 2017.
This report concerns the ex post evaluation of the European Social Fund (ESF) in the programming period. It contains a synthesis report, thematic and country reports and country fact sheets. This summary relates to a study of the use of the European Social Fund (ESF) to support lifelong learning (LLL) during the and programming periods. Drawing on the evidence presented in these country reports, the overall synthesis report provides a picture of what has been achieved by the ESF. The results reported are generally based on data and evaluations covering the period 2007 to end 2012.
Reducing regulatory and administrative burdens and promoting high standards of transparency, efficency and accountability in public administration helps to increase productivity, strengthen competitiveness and ultimately, create jobs. The European Social Fund (ESF) is a concrete European contribution to national policy reforms in the area of public administration and good governance. SCOs were introduced in the 2007–2013 programming period for ESF in order to reduce the administrative burden on Managing Authorities when implementing ESF co-funded projects and on beneficiaries.
Together with the co-financing provided by Member States, cohesion policy accounts for a very significant proportion of public investment in Europe. As part of the essential economic policy package of macro-economic and fiscal stability, structural reforms and growth enhancing measures, EU cohesion policy is making a significant contribution to investments godziny otwarcia giełdy forex in employment and growth in Europe. Employment is the most effective way of giving people independence, financial security and a sense of belonging. The European Social Fund (ESF) finances many thousands of projects to help people in difficulties and those from disadvantaged groups to get skills, to get jobs and have the same opportunities as others do.
It presents an overview of the planned implementation of SCOs during the current programming period, and the benefits that national authorities derive from them. Finally, it looks at what else needs to be done to increase the use of SCOs. The results represent the most comprehensive estimate available of the use of the SCOs in the ESF.
The purpose of this evaluation has been to provide knowledge on how ESF delivery systems work in practice, with a view to improving the capacity of the delivery systems in the Member States to attract and support https://www.google.com/search?biw=1434&bih=742&ei=5_oMXrzTH8mcmwX5ybbYAg&q=contra+asset+account&oq=contra+asset+account&gs_l=psy-ab.3..0l10.65277.65277..65507…0.2..0.68.68.1……0….2j1..gws-wiz…….0i71.y6qb2XxoxBk&ved=0ahUKEwj84vz7mOPmAhVJzqYKHfmkDSsQ4dUDCAo&uact=5 Operational Programme target groups. Here you can browse an example website automatically created for 4 classification models (random forest, gradient boosting, support vector machines, k-nearest neighbours).
In terms of projects, the number of projects using SCO is 19% for EAFRD, 65% for ESF, 50% for ERDF and 25% for CF. It is expected that at the end of the programming period SCOs will cover approximately 33% of ESF, 2% of EAFRD and 4% of ERDF-CF https://broker-review.org/ budget. The Union’s cohesion policy – funded with EUR 346 billion from the European Regional Development Fund (ERDF), the European Social Fund (ESF) and the Cohesion Fund – represents 35 % of the Union’s budget over the period .
In preparation for the programming period Member States are required to develop performance indicators and set targets for monitoring the implementation and performance http://agrotex-sklep.pl/?p=22869 of Operational Programmes (OPs). This background paper summarises the main methodologies for ESF target setting and adjusting in OPs in social inclusion.
If a model is making its’ predictions for the right reason, evidences should make much sense and it should be easy for the reader to make a story and connect the evidence to the claim. If the model is displaying evidence, that makes not much sense, it also should be a clear signal, that the model may not be trustworthy. The model prediction is significantly lower than the average model prediction.