Optimizing Hyperparameters for Machine Learning Algorithms in Production

Language: English

Published by Apprimus Verlag Apr 2022, 2022

3985550204 / 9783985550203

  • Softcover
  • New
See all details

Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

5-star seller

AbeBooks seller since January 11, 2012

View this seller's items
Softcover

Condition: New

£ 34.40

£ 19.70 shipping 
Ships from Germany to U.S.A.

Quantity: 2 available

Add to basket
Free 30-day returns

Item description from seller

This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning (ML) offers the potential to train data-based models and therefore to extract knowledge from data. Due to an increase in networking and digitalization, data and consequently the application of ML are growing in production. The creation of ML models includes several tasks that need to be conducted within data integration, data preparation, modeling, and deployment. One key design decision in this context is the selection of the hyperparameters of an ML algorithm - regardless of whether this task is conducted manually by a data scientist or automatically by an AutoML system. Therefore, data scientists and AutoML systems rely on hyperparameter optimization (HPO) techniques: algorithms that automatically identify good hyperparameters for ML algorithms. The selection of the HPO technique is of great relevance, since it can improve the final performance of an ML model by up to 62 % and reduce its errors by up to 95 %, compared to computing with default values. As the selection of the HPO technique depends on different domain-specific influences, it becomes more and more popular to use decision support systems to facilitate this selection. Since no approach exists, which covers the requirements from the production domain, the main research question of this thesis was: Can a decision support system be developed that supports in the selecting of HPO techniques in the production domain 258 pp. Englisch.

Seller Inventory # 9783985550203

Bibliographic details

Title
Optimizing Hyperparameters for Machine Learning Algorithms in Production
Author
Jonathan Krauß
Publisher
Apprimus Verlag Apr 2022
Publication year
2022
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3985550204
ISBN 13
9783985550203
Item weight
380 grams
Dimensions
210x150x13 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

Item5 to 15 business days5 to 15 business days
First item£ 19.70£ 19.70
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.

Payment methods

  • Visa
  • Mastercard
  • American Express
  • Apple Pay
  • Google Pay
  • Bank Wire Transfer
  • Check
  • Paypal

Seller's business information

BuchWeltWeit Ludwig Meier e.K.

Germany