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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

Authors (2):

Trent Hauck

Julian Avila

View More author details

Table of Contents (13) Chapters

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

Varying the classification threshold in logistic regression

Getting ready

We will use the fact that underlying the logistic regression classification, there is regression to minimize the number of times people were sent home for not having diabetes although they do. Do so by calling the predict_proba() method of the estimator:

y_pred_proba = lr.predict_proba(X_test)

This yields an array of probabilities. View the array:

y_pred_proba

array([[ 0.87110309,  0.12889691],
       [ 0.83996356,  0.16003644],
       [ 0.81821721,  0.18178279],
       [ 0.73973464,  0.26026536],
       [ 0.80392034,  0.19607966], ...

In the first row, a probability of about 0.87 is assigned to class 0 and a probability of 0.13 is assigned to 1. Note that...

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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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