Feature engineering and selection: a practical approach for predictive models
by Kuhn, Max
A primary goal of predictive modeling is to find a reliable and effective predictive relationship between an available set of features and an outcome. Ineffective feature representations and the inclusion of irrelevant features are two key data characteristics that can prevent a model from demonstrating good performance. Feature Engineering and Selection: A Practical Approach for Predictive Models provides an extensive set of techniques for uncovering effective representations of the features f…
Read More ↓Title Statements
- Full title :Feature engineering and selection : a practical approach for predictive models / Max Kuhn, Kjell Johnson.
Summary
A primary goal of predictive modeling is to find a reliable and effective predictive relationship between an available set of features and an outcome. Ineffective feature representations and the inclusion of irrelevant features are two key data characteristics that can prevent a model from demonstrating good performance. Feature Engineering and Selection: A Practical Approach for Predictive Models provides an extensive set of techniques for uncovering effective representations of the features for modeling the outcome and for finding an optimal subset of features to improve a model's predictive performance.
Read More ↓Edition Statements
- Publication Date:©2020
Publication Statements
- Publisher(s):Chapman & Hall/CRC
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:©2020
The Physical Object
- Description:xv, 297 : : illustrations (some color) ; : 26 cm.
Edition Identifiers
- ISBN:9781032090856, 1032090855
- oclc:on1255827568
- LC Classification:TJ217.6
- Dewey Decimal:629.80285513
Table of Contents
Introduction -- Illustrative example: predicting risk of ischemic stroke -- A review of the predictive modeling process -- Exploratory visualizations -- Encoding categorical predictors -- Engineering numeric predictors -- Detecting interaction effects -- Handling missing data -- Working with profile data -- Feature selection overview -- Greedy search methods -- Global search methods.
Read More ↓Subjects
Writers
More from this Series
No reviews yet. Be the first to rate this work.
Related Works
more →
Neural networks with R: smart models using CNN, RNN, deep learn…
Ciabuschi, Francesco
R for political data science: a practical guide
Econometric modelling with time series: specification, estimation and testing
Martin, Vance
R for political data science: a practical guide
R for political data science: a practical guide
Files & Downloads
-
Feature Engineering and Selection: A Practical Ap…
PDF · 20.7 MB · updated
Rate & Review