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Mastering Machine Learning with scikit-learn. - Second Edition

You're reading from Mastering Machine Learning with scikit-learn. - Second Edition

Product type Book

Published in Jul 2017

Publisher

ISBN-13 9781788299879

Pages 254 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gavin Hackeling

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. The Fundamentals of Machine Learning

2. Simple Linear Regression

3. Classification and Regression with k-Nearest Neighbors

4. Feature Extraction

5. From Simple Linear Regression to Multiple Linear Regression

6. From Linear Regression to Logistic Regression

7. Naive Bayes

8. Nonlinear Classification and Regression with Decision Trees

9. From Decision Trees to Random Forests and Other Ensemble Methods

10. The Perceptron

11. From the Perceptron to Support Vector Machines

12. From the Perceptron to Artificial Neural Networks

13. K-means

14. Dimensionality Reduction with Principal Component Analysis

Index

Evaluating the model

We have used a learning algorithm to estimate a model's parameters from training data. How can we assess whether our model is a good representation of the real relationship? Let's assume that you have found another page in your pizza journal. We will use this page's entries as a test set to measure the performance of our model. We have added a fourth column; it contains the prices predicted by our model.

Test instance	Diameter in inches	Observed price in dollars	Predicted price in dollars
`1`	`8`	`11`	`9.7759`
`2`	`9`	`8.5`	`10.7522`
`3`	`11`	`15`	`12.7048`
`4`	`16`	`18`	`17.5863`
`5`	`12`	`11`	`13.6811`

Several measures can be used to assess our model's predictive capability. We will evaluate our pizza price predictor using a measure called R-squared. Also known as the coefficient of determination, R-squared measures how close the data are to a regression line. There are several methods for calculating R-squared. In the case of simple linear regression, R-squared is equal to the square of the Pearson product-moment correlation coefficient...

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Authors (1)

Gavin Hackeling

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

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