Predictive evaluation
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Predictive evaluation ensuring training delivers business and organizational results by David J. Basarab

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Published by Berrett-Koehler Publishers in San Francisco .
Written in English

Subjects:

  • Employees,
  • Evaluation,
  • Training of,
  • Employee training personnel

Book details:

Edition Notes

StatementDave Basarab
Contributionsebrary, Inc
Classifications
LC ClassificationsHF5549.5.T7 B2937 2010eb
The Physical Object
Format[electronic resource] :
Paginationxiii, 150 p. :
Number of Pages150
ID Numbers
Open LibraryOL25563221M
ISBN 109781605098241
OCLC/WorldCa697265987

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  Predictive evaluation enables you to effectively and accurately forecast training's value to your company, measure against these predictions, establish indicators to track your progress, make midcourse corrections, and report the results in a language that Pages: His newest training evaluation book, Predictive Evaluation, spotlights his pioneering new model in predicting training success. As an expert on world-renowned Donald Kirkpatrick’s training models, he has contributed introductions and chapter content for Kirkpatrick’s recent books on training. Dave Basarab's Predictive Evaluation offers a fresh, innovative approach to training and evaluation. The book is easy to follow and clearly explains how to utilize the Predictive Evaluation model in the business world. Predictive Evaluation will be invaluable to anyone looking to invest in training and evaluation programs - and/or trying justify the need for these programs in their organizations. /5. Predictive Evaluation (PE) approach. This book covers a lot of territory, from data collection and analysis to reporting and continuous improvement efforts. It lays out the process of PE step by step, allowing even novice practitioners to implement much, if not all, of its contents. This book is practical—a “how-to” publication that begins where most evaluation books end.

Board Book $ Add to Cart. Save to Wishlist. From the Teacher Store Book Today Is Monday By. Eric Carle. Grade s. K-2 Read i ng level. E. Paperback Book $ Add to Cart. Save to Wishlist. From the Teacher Store Book Who Took the Farmer's Hat?. Predictive Evaluation. 1. Predictive Evaluation. John Stasko Spring This material has been developed by Georgia Tech HCI faculty, and continues to evolve. Contributors include Gregory Abowd, Al Badre, Jim Foley, Elizabeth Mynatt, Jeff Pierce, Colin Potts, Chris Shaw, John Stasko, and Bruce Walker. How to Evaluate Books. To evaluate a book look for: Purpose: Why was the book written? To: inform? For example: sequence of historical events, results of lengthy study or experiment; persuade? For example: to change point of view, outlook, beliefs, or behavior; .   Proper predictive models evaluation is also important because we want our model to have the same predictive ability across many different data sets. In other words, the results need to be comparable, measurable and reproducible, which are important factors for many industries with heavy regulations, such as insurance and healthcare.

In Predictive Evaluation: Ensuring Training Delivers Business and Organizational Results (Berrett-Koehler, ), author Dave Basarab explains how to begin by zeroing in on the program’s intentions: What specific goals and beliefs do you want to instill in participants? The next step is to determine what these will look like when put into action—what precise on-the-job actions and behaviors do you expect . evaluation metric, o ine evaluation, online evaluation, AUC, RIG, log-likelihood, prediction error, simulated metric, on-line advertising, sponsored search, click prediction 1. INTRODUCTION In the eld of machine learning, evaluation metrics are of-ten used to judge and compare the performance of predictive models on benchmark datasets. J. Sunil Rao, Jie Fan, in Handbook of Statistics, Clustering of Census Tracts Adds Robustness to Predictions. The next set of analyses examined predictive performance of the various estimators while allowing for clustering of census tracts with respect to community variables. The logic here is that even if an individual is incorrectly classified to a given tract, as long as the true. The Actionable Mining and Predictive Analysis process addresses unique requirements and constraints associated with the applied setting, including data access and availability, public safety-specific evaluation, and the requirement for operationally relevant and actionable output. Data privacy and security also are addressed.