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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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Toc

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

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The scikit-learn library requires input tables of two-dimensional NumPy arrays."

Any command-line input or output is written as follows:

import numpy as np #Load the numpy library for fast array
computations
import pandas as pd #Load the pandas data-analysis library
import matplotlib.pyplot as plt #Load the pyplot visualization
library

New terms and important words are shown in bold.

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.
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