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Feature Engineering Made Easy

You're reading from   Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Length 316 pages
Edition 1st Edition
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Authors (2):
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Divya Susarla Divya Susarla
Author Profile Icon Divya Susarla
Divya Susarla
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Feature Engineering FREE CHAPTER 2. Feature Understanding – What's in My Dataset? 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 9. Other Books You May Enjoy

Creating a baseline machine learning pipeline

In previous chapters, we offered to you, the reader, a single machine learning model to use throughout the chapter. In this chapter, we will do some work to find the best machine learning model for our needs and then work to enhance that model with feature selection. We will begin by importing four different machine learning models:

  • Logistic Regression
  • K-Nearest Neighbors
  • Decision Tree
  • Random Forest

The code for importing the learning models is given as follows:

# Import four machine learning models
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

Once we are finished importing these modules, we will run them through our get_best_model_and_accuracy functions to get a baseline on...

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