|
Books > Business & Economics > Finance & accounting > Finance > Insurance
|
Buy Now
Effective Statistical Learning Methods for Actuaries II - Tree-Based Methods and Extensions (Paperback, 1st ed. 2020)
Loot Price: R1,408
Discovery Miles 14 080
|
|
|
Effective Statistical Learning Methods for Actuaries II - Tree-Based Methods and Extensions (Paperback, 1st ed. 2020)
Series: Springer Actuarial
Expected to ship within 18 - 22 working days
|
This book summarizes the state of the art in tree-based methods for
insurance: regression trees, random forests and boosting methods.
It also exhibits the tools which make it possible to assess the
predictive performance of tree-based models. Actuaries need these
advanced analytical tools to turn the massive data sets now at
their disposal into opportunities. The exposition alternates
between methodological aspects and numerical illustrations or case
studies. All numerical illustrations are performed with the R
statistical software. The technical prerequisites are kept at a
reasonable level in order to reach a broad readership. In
particular, master's students in actuarial sciences and actuaries
wishing to update their skills in machine learning will find the
book useful. This is the second of three volumes entitled Effective
Statistical Learning Methods for Actuaries. Written by actuaries
for actuaries, this series offers a comprehensive overview of
insurance data analytics with applications to P&C, life and
health insurance.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
