The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset o
An Empirical Analysis of Feature Engineering for Predictive Modeling
โ Scribed by Heaton, Jeff
- Tongue
- English
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- 6
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- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
I Introduction
II Background and Prior Work
III Experiment Design and Methodology
III-A Counts
III-B Differences and Ratios
III-C Distance Between Quadratic Roots
III-D Distance Formula
III-E Logarithms and Power Functions
III-F Max of Inputs
III-G Polynomials
III-H Rational Differences and Polynomials
IV Results Analysis
IV-A Neural Network Results
IV-B Support Vector Machine Results
IV-C Random Forest Results
IV-D Gradient Boosted Machine
V Conclusion & Further Research
References
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I found the root cause of many challenges faced by my students who recently transitioned into data science and machine learning. I have tried to address these issues in my book and would like to dedicate this book to all my students for all the love and respect I have received.
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