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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
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Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn FREE CHAPTER 2. Predicting Categories with K-Nearest Neighbors 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

Predicting Numeric Outcomes with Linear Regression

graph_from_dot_data() function on the Linear regression is used to predict a continuous numeric value from a set of input features. This machine learning algorithm is fundamental to statisticians when it comes to predicting numeric outcomes. Although advanced algorithms such as neural networks and deep learning have taken the place of linear regression in modern times, the algorithm is still key when it comes to providing you with the foundations for neural networks and deep learning.

The key benefit of building machine learning models with the linear regression algorithm, as opposed to neural networks and deep learning, is that it is highly interpretable. Interpretability helps you, as the machine learning practitioner, to understand how the different input variables behave when it comes to predicting output.

The linear regression...

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