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

Performing Principal Component Analysis

There are several terms that we must define before discussing how principal component analysis works.

Variance, Covariance, and Covariance Matrices

Recall that variance is a measure of how a set of values are spread out. Variance is calculated as the average of the squared differences of the values and mean of the values, as per the following equation:

Covariance is a measure of how much two variables change together; it is a measure of the strength of the correlation between two sets of variables. If the covariance of two variables is zero, the variables are uncorrelated. Note that uncorrelated variables are not necessarily independent, as correlation is only a measure of linear dependence. The covariance of two variables is calculated using the following equation:

If the covariance is nonzero, the sign indicates whether the variables are positively or negatively correlated. When two variables are positively correlated, one increases as the other increases...

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