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Supervised Machine Learning

Tanya Kolosova | Samuel Berestizhevsky
€173.60
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A01=Samuel Berestizhevsky
A01=Tanya Kolosova
advanced R programming
Artificial Intelligence
Author_Samuel Berestizhevsky
Author_Tanya Kolosova
Bias-variance tradeoff
Bootstrap Estimate
Category=PBT
Category=UYQM
Cox Hazard Model
data contamination analysis
Data Dictionary
Data Dictionary Table
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
feature selection strategies
Firth Logistic Regression
hyper-parameters
insurance risk modeling
Linear Mixed Model
machine learning
Machine Learning Methods
MCD Estimate
MCD Method
optimization framework
outlier detection methods
Polynomial Kernel
Primary Key
PROC SURVEYSELECT
Relational Data Model
robust classifier optimization framework
SAS Data Set
SAS Dataset
SAS Macro
SAS Macro Program
SID
statistical experiment design
statistical experiments
Supervised Machine Learning
Support Vector Machine
SVM Function
SVM Method
SVM Output
Testing Datasets
Training Datasets

Product details

  • ISBN 9780367277321
  • Weight: 1080g
  • Dimensions: 156 x 234mm
  • Publication Date: 22 Sep 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers.

Key Features:

  • Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data

  • Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments

  • Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias

  • Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks

  • Computer programs in R and SAS that create AI framework are available on GitHub
About Samuel Berestizhevsky | Tanya Kolosova

Tanya Kolosova is a statistician, software engineer, an educator, and a co-author of two books on statistical analysis and metadata-based applications development using SAS. Tanya is an actionable analytics expert, she has extensive knowledge of software development methods and technologies, artificial intelligence methods and algorithms, and statistically designed experiments.

Samuel Berestizhevsky is a statistician, researcher and software engineer. Together with Tanya, Samuel co-authored two books on statistical analysis and metadata-based applications development using SAS. Samuel is an innovator and an expert in the area of automated actionable analytics and artificial intelligence solutions. His extensive knowledge of software development methods, technologies and algorithms allows him to develop solutions on the cutting edge of science.

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