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scikit-learn Cookbook , Second Edition

You're reading from scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787286382

Length 374 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Trent Hauck

View More author details

Table of Contents (13) Chapters

Preface

1. High-Performance Machine Learning – NumPy

2. Pre-Model Workflow and Pre-Processing FREE CHAPTER

3. Dimensionality Reduction

4. Linear Models with scikit-learn

5. Linear Models – Logistic Regression

6. Building Models with Distance Metrics

7. Cross-Validation and Post-Model Workflow

8. Support Vector Machines

9. Tree Algorithms and Ensembles

10. Text and Multiclass Classification with scikit-learn

11. Neural Networks

12. Create a Simple Estimator

Using factor analysis for decomposition

Factor analysis is another technique that we can use to reduce dimensionality. However, factor analysis makes assumptions and PCA does not. The basic assumption is that there are implicit features responsible for the features of the dataset.

This recipe will boil down to the explicit features from our samples in an attempt to understand the independent variables as much as the dependent variables.

Getting ready

To compare PCA and factor analysis, let's use the iris dataset again, but we'll first need to load the FactorAnalysis class:

from sklearn import datasets
iris = datasets.load_iris()
iris_X = iris.data
from sklearn.decomposition import FactorAnalysis

...

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Authors (1)

Trent Hauck

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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