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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 (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. High-Performance Machine Learning – NumPy FREE CHAPTER

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

Receiver operating characteristic – ROC analysis

Along the same lines of examining NPV, there are standard measures that examine cells within a confusion matrix.

Getting ready

Sensitivity

Sensitivity, like NPV in the previous section, is a mathematical function of the confusion matrix cells. Sensitivity is the proportion of people who took the test with a condition and were correctly labeled as having the condition, diabetes in this case:

Mathematically, it is the ratio of patients correctly labeled as having a condition (TP) divided by the total number of people who actually have the condition (TP + FN). First, recall the confusion matrix cells. Focus on the Truth row, which corresponds to all people who have diabetes:

Consequently:

Another name for sensitivity is true positive rate (TPR).

A visual perspective

Another perspective on the confusion matrix is very visual. Let us visualize both the positive class having diabetes and the negative class having no diabetes with histograms (on the left...

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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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