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scikit-learn Cookbook - Second Edition

You're reading from  scikit-learn Cookbook - Second Edition

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Pages 374 pages
Edition 2nd Edition
Languages
Author (1):
Trent Hauck Trent Hauck
Profile icon Trent Hauck
Toc

Table of Contents (13) Chapters close

Preface 1. High-Performance Machine Learning – NumPy 2. Pre-Model Workflow and Pre-Processing 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

Introduction

What is data, and what are we doing with it?

A simple answer is that we attempt to place our data as points on paper, graph them, think, and look for simple explanations that approximate the data well. The simple geometric line of F=ma (force being proportional to acceleration) explained a lot of noisy data for hundreds of years. I tend to think of data science as data compression at times.

Sometimes, when a machine is given only win-lose outcomes (of winning games of checkers, for example) and trained, I think of artificial intelligence. It is never taught explicit directions on how to play to win in such a case.

This chapter deals with the pre-processing of data in scikit-learn. Some questions you can ask about your dataset are as follows:

  • Are there missing values in your dataset?
  • Are there outliers (points far away from the others) in your set?
  • What are the variables...
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