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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 SGD for classification

The stochastic gradient descent (SGD) is a fundamental technique used to fit a model for regression. There are natural connections between SGD for classification or regression.

Getting ready

In regression, we minimized a cost function that penalized for bad choices on a continuous scale, but for classification, we'll minimize a cost function that penalizes for two (or more) cases.

How to do it...

  1. First, let's create some very basic data:
from sklearn import datasets
X, y = datasets.make_classification(n_samples = 500)
  1. Split the...
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