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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

Plotting an ROC curve without context

How to do it...

An ROC curve is a diagnostic tool for any classifier without any context. No context means that we do not know yet which error type (FP or FN) is less desirable yet. Let us plot it right away using a vector of probabilities, y_pred_proba[:,1]:

from sklearn.metrics import roc_curve

fpr, tpr, ths = roc_curve(y_test, y_pred_proba[:,1])
plt.plot(fpr,tpr)

The ROC is a plot of the FPR (false alarms) in the x axis and TPR (finding everyone with the condition who really has it) in the y axis. Without context, it is a tool to measure classifier performance.

Perfect classifier

...

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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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