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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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Toc

Table of Contents (15) Chapters Close

Preface 1. The Fundamentals of Machine Learning FREE CHAPTER 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

Classification with KNN

Recall from Chapter 1, The Fundamentals of Machine Learning that the goal of classification tasks is to use one or more features to predict the value of a discrete response variable. Let's work through a toy classification problem. Assume that you must use a person's height and weight to predict his or her sex. This problem is called binary classification because the response variable can take one of two labels. The following table records nine training instances:

Height

Weight

Label

158 cm

64 kg

male

170 cm

66 kg

male

183 cm

84 kg

male

191 cm

80 kg

male

155 cm

49 kg

female

163 cm

59 kg

female

180 cm

67 kg

female

158 cm

54 kg

female

178 cm

77 kg

female

 

Unlike the previous chapter's simple linear regression problem, we are now using features from two...

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