Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

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Table of Contents (9 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Getting Started with Python and Machine Learning
Getting Started with Python and Machine Learning
What is machine learning and why do we need it?
A very high level overview of machine learning
A brief history of the development of machine learning algorithms
Generalizing with data
Overfitting, underfitting and the bias-variance tradeoff
Avoid overfitting with feature selection and dimensionality reduction
Preprocessing, exploration, and feature engineering
Combining models
Installing software and setting up
Troubleshooting and asking for help
Summary
2
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
What is NLP?
Touring powerful NLP libraries in Python
The newsgroups data
Getting the data
Thinking about features
Visualization
Data preprocessing
Clustering
Topic modeling
Summary
3
Spam Email Detection with Naive Bayes
Spam Email Detection with Naive Bayes
Getting started with classification
Types of classification
Applications of text classification
Exploring naive Bayes
Bayes' theorem by examples
The mechanics of naive Bayes
The naive Bayes implementations
Classifier performance evaluation
Model tuning and cross-validation
Summary
4
News Topic Classification with Support Vector Machine
News Topic Classification with Support Vector Machine
Recap and inverse document frequency
Support vector machine
News topic classification with support vector machine
More examples - fetal state classification on cardiotocography with SVM
Summary
5
Click-Through Prediction with Tree-Based Algorithms
Click-Through Prediction with Tree-Based Algorithms
Brief overview of advertising click-through prediction
Getting started with two types of data, numerical and categorical
Decision tree classifier
Click-through prediction with decision tree
Random forest - feature bagging of decision tree
Summary
6
Click-Through Prediction with Logistic Regression
Click-Through Prediction with Logistic Regression
One-hot encoding - converting categorical features to numerical
Logistic regression classifier
Click-through prediction with logistic regression by gradient descent
Feature selection via random forest
Summary
7
Stock Price Prediction with Regression Algorithms
Stock Price Prediction with Regression Algorithms
Brief overview of the stock market and stock price
What is regression?
Predicting stock price with regression algorithms
Summary
8
Best Practices
Best Practices
Machine learning workflow
Best practices in the data preparation stage
Best practices in the training sets generation stage
Best practices in the model training, evaluation, and selection stage
Best practices in the deployment and monitoring stage
Summary

Classifier performance evaluation

So far, we have covered the first machine learning classifier and evaluated its performance by prediction accuracy in-depth. Beyond accuracy, there are several measurements that give us more insights and avoid class imbalance effects.

Confusion matrix summarizes testing instances by their predicted values and true values, presented as a contingency table:

To illustrate, we compute the confusion matrix of our naive Bayes classifier. Here the scikit-learn confusion_matrix function is used, but it is very easy to code it ourselves:

>>> from sklearn.metrics import confusion_matrix
>>> confusion_matrix(Y_test, prediction, labels=[0, 1])
array([[1098,   93],
       [  43,  473]])

Note that we consider 1 the spam class to be positive. From the confusion matrix, for example, there are 93 false positive cases (where it misinterprets a legitimate email as a spam one), and...

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