Book Image

Mastering Machine Learning with scikit-learn

By : Gavin Hackeling
Book Image

Mastering Machine Learning with scikit-learn

By: Gavin Hackeling

Overview of this book

<p>This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.</p> <p>You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.</p> <p>By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning.</p>

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Mastering Machine Learning with scikit-learn
Gavin Hackeling
Oct, 2014 | 238 pages
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Table of Contents (17 chapters)
Mastering Machine Learning with scikit-learn
Mastering Machine Learning with scikit-learn
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The Fundamentals of Machine Learning
The Fundamentals of Machine Learning
Learning from experience
Machine learning tasks
Training data and test data
Performance measures, bias, and variance
An introduction to scikit-learn
Installing scikit-learn
Installing pandas and matplotlib
Summary
2
Linear Regression
Linear Regression
Simple linear regression
Evaluating the model
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Fitting models with gradient descent
Summary
3
Feature Extraction and Preprocessing
Feature Extraction and Preprocessing
Extracting features from categorical variables
Extracting features from text
Extracting features from images
Data standardization
Summary
4
From Linear Regression to Logistic Regression
From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-label classification and problem transformation
Summary
5
Nonlinear Classification and Regression with Decision Trees
Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Decision trees with scikit-learn
Summary
6
Clustering with K-Means
Clustering with K-Means
Clustering with the K-Means algorithm
Evaluating clusters
Image quantization
Clustering to learn features
Summary
7
Dimensionality Reduction with PCA
Dimensionality Reduction with PCA
An overview of PCA
Performing Principal Component Analysis
Using PCA to visualize high-dimensional data
Face recognition with PCA
Summary
8
The Perceptron
The Perceptron
Activation functions
Binary classification with the perceptron
Limitations of the perceptron
Summary
9
From the Perceptron to Support Vector Machines
From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Classifying characters in scikit-learn
Summary
10
From the Perceptron to Artificial Neural Networks
From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Approximating XOR with Multilayer perceptrons
Classifying handwritten digits
Summary
Index
Index

Kernels and the kernel trick

Recall that the perceptron separates the instances of the positive class from the instances of the negative class using a hyperplane as a decision boundary. The decision boundary is given by the following equation:

Predictions are made using the following function:

Note that previously we expressed the inner product as . To be consistent with the notational conventions used for support vector machines, we will adopt the former notation in this chapter.

While the proof is beyond the scope of this chapter, we can write the model differently. The following expression of the model is called the dual form. The expression we used previously is the primal form:

The most important difference between the primal and dual forms is that the primal form computes the inner product of the model parameters and the test instance's feature vector, while the dual form computes the inner product of the training instances and the test instance's feature vector. Shortly, we will exploit...

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