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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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
Author Profile Icon Julian Avila
Julian Avila
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Table of Contents (13) Chapters Close

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

Using dummy estimators to compare results

This recipe is about creating fake estimators; this isn't the pretty or exciting stuff, but it is worthwhile having a reference point for the model you'll eventually build.

Getting ready

In this recipe, we'll perform the following tasks:

  1. Create some random data.
  2. Fit the various dummy estimators.

We'll perform these two steps for regression data and classification data.

How to do it...

  1. First, we'll create the random data:
from sklearn.datasets import make_regression, make_classification

X, y = make_regression...
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