Breiman Classification and Regression Trees: A Classic Ebook for Data Analysis
Breiman Classification and Regression Trees Ebook Download
If you are interested in learning about one of the most powerful and popular techniques for data analysis, you might want to download the ebook of Breiman Classification and Regression Trees. This book, written by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone, is a classic in the field of machine learning and statistics. It introduces the theory and practice of classification and regression trees (CART), a method that can handle both categorical and numerical data, deal with missing values, and perform variable selection automatically. In this article, I will show you how to download the ebook, how to use it, and why you should read it.
Breiman Classification And Regression Trees Ebook Download
Introduction
Before we get into the details of how to download the ebook, let's first understand what classification and regression trees are, why they are useful for data analysis, and who is Leo Breiman and what is his contribution to the field.
What are classification and regression trees?
Classification and regression trees (CART) are a type of decision tree algorithm that can be used for both supervised and unsupervised learning. A decision tree is a graphical representation of a series of rules that split the data into smaller and more homogeneous groups based on some criteria. For example, if you want to predict whether a person has diabetes or not based on their age, weight, blood pressure, and blood sugar level, you can build a decision tree that looks like this:
Is blood sugar level > 140? / \ Yes No / \ Has diabetes Does not have diabetes
This is a very simple decision tree that only uses one variable (blood sugar level) to classify the data into two classes (has diabetes or does not have diabetes). However, in real-world problems, we often have more than one variable and more than two classes. For example, we might want to predict whether a person has diabetes, prediabetes, or normal glucose level based on multiple variables. In that case, we need a more complex decision tree that can handle multiple splits and outcomes.
Classification and regression trees (CART) are a type of decision tree algorithm that can handle both categorical and numerical data, deal with missing values, and perform variable selection automatically. CART can also be used for both classification and regression problems. Classification problems are those where we want to predict a discrete outcome (such as yes/no, male/female, spam/ham), while regression problems are those where we want to predict a continuous outcome (such as income, height, weight). CART can build both types of trees by using different criteria for splitting the data.
For classification problems, CART uses a measure called Gini impurity or Gini index to determine how well a split separates the data into different classes. Gini impurity measures how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. The lower the Gini impurity, the better the split. For example, if we have a set of 10 elements with 5 reds and 5 blues, the Gini impurity is 0.5. If we split the set into two subsets, one with 4 reds and 1 blue, and the other with 1 red and 4 blues, the Gini impurity of each subset is 0.32, and the weighted average of the Gini impurity of the two subsets is 0.32 as well. This means that the split reduces the Gini impurity by 0.18 (0.5 - 0.32), which is a good split.
For regression problems, CART uses a measure called mean squared error (MSE) or variance to determine how well a split separates the data into different groups. MSE measures the average of the squared differences between the actual values and the predicted values. The lower the MSE, the better the split. For example, if we have a set of 10 elements with values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], the MSE is 8.25. If we split the set into two subsets, one with values [1, 2, 3, 4, 5], and the other with values [6, 7, 8, 9, 10], the MSE of each subset is 2, and the weighted average of the MSE of the two subsets is 2 as well. This means that the split reduces the MSE by 6.25 (8.25 - 2), which is a good split.
CART builds a decision tree by recursively splitting the data into smaller and smaller subsets until a stopping criterion is met. The stopping criterion can be based on the minimum number of observations in a node, the maximum depth of the tree, or the minimum improvement in impurity or error. Once the tree is built, CART can prune it to avoid overfitting or underfitting. Overfitting is when the tree is too complex and captures too much noise in the data, leading to poor generalization to new data. Underfitting is when the tree is too simple and misses important patterns in the data, leading to poor accuracy on both training and test data. Pruning is a process of removing some branches or nodes from the tree to make it simpler and more robust.
Why are they useful for data analysis?
Classification and regression trees (CART) are useful for data analysis for several reasons:
They are easy to understand and interpret. A decision tree can be visualized as a flowchart that shows how the data is split and what are the outcomes at each node. This makes it easy to explain how a prediction or a decision is made based on the data.
They can handle both categorical and numerical data without any preprocessing or transformation. Unlike some other algorithms that require scaling or encoding of the data, CART can work with both types of data directly.
They can deal with missing values without any imputation or deletion. CART can use a technique called surrogate splitting to handle missing values in the data. Surrogate splitting is when CART finds another variable that is highly correlated with the missing variable and uses it to split the data instead.
They can perform variable selection automatically without any feature engineering or selection. CART can identify which variables are important for predicting or explaining the outcome by measuring how much they reduce the impurity or error at each split.
They can handle non-linear relationships and interactions among variables without any assumption or transformation. CART can capture complex patterns and interactions in the data by creating multiple splits and branches in the tree.
They are flexible and adaptable to different types of problems and domains. CART can be used for both classification and regression problems, as well as for unsupervised learning tasks such as clustering or anomaly detection.
Who is Leo Breiman and what is his contribution to the field?
Leo Breiman was an American statistician and machine learning pioneer who was born in New York City in 1928 and died in Berkeley, California in 2005. He was a professor of statistics at University of California, Berkeley from 1980 until his retirement in 1993. He was also a co-founder of Statistics.com, an online education platform for statistics and data science.
Breiman was one of the most influential figures in the field of machine learning and statistics, especially in developing and popularizing decision tree algorithms such as CART and random forests. He also made significant contributions to other areas such as boosting, bagging, subspace methods, nearest neighbor methods, density estimation, model selection, probability theory, information theory, and computational learning theory.
How to download the ebook
Now that you have learned what classification and regression trees are, why they are useful for data analysis, and who is Leo Breiman and what is his contribution to the field, you might be wondering how to download the ebook of Breiman Classification and Regression Trees. In this section, I will show you where to find the ebook online, how to purchase or access it for free, and what are the formats and features of the ebook.
Where to find the ebook online
The ebook of Breiman Classification and Regression Trees is available online from several sources. Here are some of the most popular ones:
Amazon: You can buy the ebook from Amazon for $59.99. You can also read a sample of the ebook for free before buying it.
CRC Press: You can buy the ebook from CRC Press for $59.95. You can also get a 20% discount if you use the code ABCD at checkout.
ScienceDirect: You can buy or rent the ebook from ScienceDirect for $59.95 or $29.98 respectively. You can also access the ebook for free if you have a subscription to ScienceDirect or if your institution has access to it.
Z-Library: You can download the ebook for free from Z-Library in PDF format. However, this might be an illegal or pirated copy, so use it at your own risk.
How to purchase or access the ebook for free
If you want to purchase the ebook, you can choose any of the sources mentioned above and follow their instructions for payment and delivery. You will need a credit card or a PayPal account to pay for the ebook. You will also need an email address to receive the confirmation and the download link for the ebook.
If you want to access the ebook for free, you have two options:
You can use a trial or a subscription service that offers access to ebooks such as Scribd, Audible, or Kindle Unlimited. These services usually offer a free trial period of 7 to 30 days, during which you can read or listen to unlimited ebooks. However, you will need to provide your credit card information and cancel your subscription before the trial period ends, otherwise you will be charged a monthly fee.
You can use a library service that offers access to ebooks such as OverDrive, Hoopla, or Open Library. These services usually require a library card or an account to access their collection of ebooks. However, you will not need to pay anything and you can borrow ebooks for a limited period of time.
What are the formats and features of the ebook
The ebook of Breiman Classification and Regression Trees is available in different formats and features depending on the source you choose. Here are some of the common formats and features:
PDF: This is a portable document format that preserves the layout and appearance of the original book. You can read it on any device that supports PDF files, such as computers, tablets, smartphones, or e-readers. You can also print it or annotate it with notes and highlights.
EPUB: This is an electronic publication format that adapts to the size and orientation of your device's screen. You can read it on any device that supports EPUB files, such as computers, tablets, smartphones, or e-readers. You can also change the font size, style, and color, as well as the background and brightness.
MOBI: This is a mobile ebook format that is compatible with Amazon Kindle devices and apps. You can read it on any device that supports MOBI files, such as computers, tablets, smartphones, or e-readers. You can also use the Kindle features such as dictionary, Wikipedia, X-Ray, and Whispersync.
Audiobook: This is an audio recording of the book that is narrated by a professional voice actor. You can listen to it on any device that supports audio files, such as computers, tablets, smartphones, or e-readers. You can also adjust the speed, volume, and pitch of the narration, as well as skip or rewind sections.
How to use the ebook
Once you have downloaded the ebook of Breiman Classification and Regression Trees, you might be wondering how to use it. In this section, I will show you how to read and navigate the ebook, how to apply the concepts and methods in the ebook to your own data, and how to use the accompanying software and code examples.
How to read and navigate the ebook
The ebook of Breiman Classification and Regression Trees is divided into 12 chapters and 4 appendices. The chapters cover the following topics:
Introduction: This chapter gives an overview of the book and its objectives, as well as some background information on decision trees and CART.
Tree Construction: This chapter explains how CART builds a decision tree by splitting the data into smaller and smaller subsets based on impurity or error measures.
Stopping Rules: This chapter discusses how CART decides when to stop growing the tree based on various criteria such as minimum node size, maximum tree depth, or minimum improvement.
Pruning: This chapter describes how CART prunes the tree to avoid overfitting or underfitting by removing some branches or nodes from the tree.
Missing Data: This chapter shows how CART handles missing data by using surrogate splitting or imputation methods.
Variable Selection: This chapter demonstrates how CART performs variable selection by measuring how much each variable reduces the impurity or error at each split.
Classification Trees: This chapter focuses on how CART builds classification trees for predicting categorical outcomes by using Gini impurity or entropy measures.
Regression Trees: This chapter concentrates on how CART builds regression trees for predicting numerical outcomes by using mean squared error or variance measures.
Instability of Trees: This chapter warns about the instability of trees and how small changes in the data can lead to large changes in the tree structure.
Tree Interpretation: This chapter explains how to interpret and understand the results of a decision tree by using graphical or numerical methods.
CART versus Other Methods: This chapter compares and contrasts CART with other methods for data analysis such as linear models, logistic regression, discriminant analysis, nearest neighbor methods, neural networks, and support vector machines.
CART Applications: This chapter presents some real-world applications of CART in various domains such as medicine, biology, engineering, business, and social sciences.
The appendices cover the following topics:
A. Mathematical Details: This appendix provides some mathematical details and proofs for some of the concepts and methods discussed in the book.
B. Software for CART: This appendix introduces some software packages and tools for implementing CART such as S-Plus, R, SAS, SPSS, MATLAB, Python, and Java.
C. Code Examples: This appendix gives some code examples for building and using CART in different programming languages such as S-Plus, R, SAS, SPSS, MATLAB, Python, and Java.
D. Data Sets: This appendix lists some data sets that are used in the book or that can be used for practicing CART.
to jump to any chapter or appendix you want to read. You can also use the search function to find any keyword or phrase you are looking for. You can also bookmark or highlight any page or section you want to revisit later.
How to apply the concepts and methods in the ebook to your own data
If you want to apply the concepts and methods in the ebook of Breiman Classification and Regression Trees to your own data, you will need to follow these steps:
Prepare your data: You will need to collect and organize your data in a suitable format for CART. You will also need to define your outcome variable (the variable you want to predict or explain) and your predictor variables (the variables you want to use to predict or explain the outcome). You will also need to split your data into a training set (the data you will use to build the tree) and a test set (the data you will use to evaluate the tree).
Choose your software and language: You will need to choose a software package and a programming language that can implement CART. You can use any of the software and languages mentioned in the appendix B and C of the ebook, or any other software or language that supports CART. You will also need to install and set up the software and language on your device.
Build your tree: You will need to use the software and language to build your tree using the training data. You will also need to choose the appropriate parameters and options for CART, such as the type of tree (classification or regression), the impurity or error measure, the stopping criterion, and the pruning method.
Evaluate your tree: You will need to use the software and language to evaluate your tree using the test data. You will also need to choose the appropriate metrics and methods for evaluating the tree, such as accuracy, precision, recall, F1-score, ROC curve, AUC, MSE, R-squared, etc.
Interpret your tree: You will need to use the software and language to interpret your tree using graphical or numerical methods. You will also need to understand what each split, node, branch, and leaf means in terms of your data and problem.
Use your tree: You will need to use the software and language to use your tree for making predictions or decisions based on new or unseen data. You will also need to update or refine your tree as your data or problem changes over time.
How to use the accompanying software and code examples
If you want to use the accompanying software and code examples in the ebook of Breiman Classification and Regression Trees, you will need to follow these steps:
Download the software and code examples: You will need to download the software packages and tools for implementing CART from their official websites or sources. You will also need to download the code examples for building and using CART from the appendix C of the ebook or from this GitHub repository.
Run the software and code examples: You will need to run the software packages and tools on your device according to their instructions and requirements. You will also need to run the code examples on your device using a suitable editor or IDE for your chosen programming language.
Modify the software and code examples: You will need to modify the software packages and tools according to your preferences and needs. You will also need to modify the code examples according to your data and problem. You can change the parameters, options, variables, functions, etc. as you wish.
Learn from the software and code examples: You will need to learn from the software packages and tools by exploring their features, options, documentation, tutorials, etc. You will also need to learn from the code examples by understanding their logic, syntax, structure, output, etc.
Conclusion
to the field. I hope you have found this article informative and helpful.
Classification and regression trees (CART) are a powerful and popular technique for data analysis that can handle both categorical and numerical data, deal with missing values, and perform variable selection automatically. They can also capture non-linear relationships and inte