Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By : Gavin Hackeling
Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By: Gavin Hackeling

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

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Table of Contents (22 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
The Fundamentals of Machine Learning
The Fundamentals of Machine Learning
Defining machine learning
Learning from experience
Machine learning tasks
Training data, testing data, and validation data
Bias and variance
An introduction to scikit-learn
Installing scikit-learn
Installing pandas, Pillow, NLTK, and matplotlib
Summary
2
Simple Linear Regression
Simple Linear Regression
Simple linear regression
Evaluating the model
Summary
3
Classification and Regression with k-Nearest Neighbors
Classification and Regression with k-Nearest Neighbors
K-Nearest Neighbors
Lazy learning and non-parametric models
Classification with KNN
Regression with KNN
Summary
4
Feature Extraction
Feature Extraction
Extracting features from categorical variables
Standardizing features
Extracting features from text
Extracting features from images
Summary
5
From Simple Linear Regression to Multiple Linear Regression
From Simple Linear Regression to Multiple Linear Regression
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Gradient descent
Summary
6
From Linear Regression to Logistic Regression
From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Tuning models with grid search
Multi-class classification
Multi-label classification and problem transformation
Summary
7
Naive Bayes
Naive Bayes
Bayes' theorem
Generative and discriminative models
Naive Bayes
Naive Bayes with scikit-learn
Summary
8
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
9
From Decision Trees to Random Forests and Other Ensemble Methods
From Decision Trees to Random Forests and Other Ensemble Methods
Bagging
Boosting
Stacking
Summary
10
The Perceptron
The Perceptron
The perceptron
Limitations of the perceptron
Summary
11
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
12
From the Perceptron to Artificial Neural Networks
From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feed-forward and feedback ANNs
Multi-layer perceptrons
Training multi-layer perceptrons
Summary
13
K-means
K-means
Clustering
K-means
Evaluating clusters
Image quantization
Clustering to learn features
Summary
14
Dimensionality Reduction with Principal Component Analysis
Dimensionality Reduction with Principal Component Analysis
Principal component analysis
Visualizing high-dimensional data with PCA
Face recognition with PCA
Summary
Index
Index

Naive Bayes

In the first section of this chapter, we described Bayes' theorem. Recall that it is given by the following:

Let's rewrite Bayes' theorem in terms that are more natural for a classification task:

In the preceding formula, y is the positive class, x1 is the first feature for the instance, and n is the number of features. P(B) is constant for all inputs, so we can omit it; the probability of observing a particular feature in the training set does not vary for different test instances. This leaves two terms: the prior class probability, P(y), and the conditional probability, P(x1, ..., xn|y). Naive Bayes estimates these terms using maximum a posteriori estimation. P(y) is simply the frequency of each class in the training set. For categorical features, P(xi|y) is simply the frequency of the feature in the training instances belonging to that class. Its estimate is given by the following formula:

The numerator is the number of times that the feature appears in training samples of class...

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