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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

Evaluating the linear regression model

In this recipe, we'll look at how well our regression fits the underlying data. We fitted a regression in the last recipe, but didn't pay much attention to how well we actually did it. The first question after we fitted the model was clearly, how well does the model fit? In this recipe, we'll examine this question.

Getting ready

Let's use the lr object and Boston dataset—reach back into your code from the Fitting a line through data recipe. The lr object will have a lot of useful methods now that the model has been fit.

How to do it...

...
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