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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 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

Visualizing high-dimensional data with PCA

It is easy to discover patterns by visualizing data with two or three dimensions. A high-dimensional dataset cannot be represented graphically, but we can still gain some insights into its structure by reducing it to two or three principal components. Collected in 1936, Fisher's Iris dataset is a collection of fifty samples from each of three species of Iris: Iris setosa, Iris virginica, and Iris versicolor. The explanatory variables are measurements of the length and width of the petals and sepals of the flowers. The Iris dataset is commonly used to test classification models, and is included with scikit-learn. Let's reduce the iris dataset's four dimensions so that we can visualize it in two dimensions. First we load the built-in iris dataset and instantiate a PCA estimator. The PCA class takes the number of principal...

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