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Mastering Machine Learning with scikit-learn

You're reading from   Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn

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Product type Paperback
Published in Jul 2017
Publisher
ISBN-13 9781788299879
Length 254 pages
Edition 2nd Edition
Languages
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Author (1):
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Gavin Hackeling Gavin Hackeling
Author Profile Icon Gavin Hackeling
Gavin Hackeling
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Table of Contents (15) Chapters Close

Preface 1. The Fundamentals of Machine Learning 2. Simple Linear Regression FREE CHAPTER 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

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...

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Mastering Machine Learning with scikit-learn - Second Edition
Published in: Jul 2017
Publisher:
ISBN-13: 9781788299879
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