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Feature Engineering Made Easy

You're reading from  Feature Engineering Made Easy

Product type Book
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Pages 316 pages
Edition 1st Edition
Languages
Authors (2):
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
Divya Susarla Divya Susarla
Profile icon Divya Susarla
View More author details
Toc

Table of Contents (14) Chapters close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Introduction to Feature Engineering 2. Feature Understanding – What's in My Dataset? 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 1. Other Books You May Enjoy

LDA versus PCA – iris dataset


Finally, we arrive at the moment where we can try using both PCA and LDA in our machine learning pipelines. Because we have been working with the iris dataset extensively in this chapter, we will continue to demonstrate the utility of both LDA and PCA as feature transformational pre-processing steps for supervised and unsupervised machine learning.

We will start with supervised machine learning and attempt to build a classifier to recognize the species of flower given the four quantitative flower traits:

  1. We begin by importing three modules from scikit-learn:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score

We will use KNN as our supervised model and the pipeline module to combine our KNN model with our feature transformation tools to create machine learning pipelines that can be cross-validated using the cross_val_score module. We will try a few different machine learning...

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