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Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees
Ifyou want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday.
From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services.
They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk.
Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact.
This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.
From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services.
They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk.
Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact.
This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.
170 pages, Kindle Edition
Published December 20, 2017
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522 people want to read
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Displaying 1 - 30 of 32 reviews
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March 23, 2020Decision tree for voting 2016:
from Maru Kun's review of /Before the Storm/
from Maru Kun's review of /Before the Storm/
January 23, 2018
Maybe, like me, you never got on with mathematics at school. In my schooldays, my problems in the subject were threefold: 1) a bad teacher 2) an active resistance to anything I didn’t find stimulating 3) a lack of awareness of how to work around dyscalculia. So instead, I challenged my teacher to prove things (he refused and/or was unable; this book in contrast does not resort to “it just works this way� explanations), I accused him of witchcraft when he produced correct numeric answers with no demonstration of how things worked, and I generally struggled with anything containing numbers.
Here instead, everything is presented in a clear and simple fashion—as the title suggests, largely visual—minimizing the need to juggle a lot of numbers and instead working chiefly with concepts, which I can grasp much more readily. Where numbers are necessary, they’re not onerous and they’re nothing whose calculations one couldn’t do on a phone if necessary.
In short, a clear and engaging primer in how decision trees and random forests work and, as a bonus, how they can be used in Python—as with other books in the series, again without expecting any deep knowledge of programming.
If only books like this were used in schools, resulting in people better understanding stats and probability, the world might have a lot fewer problems than it does!
Here instead, everything is presented in a clear and simple fashion—as the title suggests, largely visual—minimizing the need to juggle a lot of numbers and instead working chiefly with concepts, which I can grasp much more readily. Where numbers are necessary, they’re not onerous and they’re nothing whose calculations one couldn’t do on a phone if necessary.
In short, a clear and engaging primer in how decision trees and random forests work and, as a bonus, how they can be used in Python—as with other books in the series, again without expecting any deep knowledge of programming.
If only books like this were used in schools, resulting in people better understanding stats and probability, the world might have a lot fewer problems than it does!
January 10, 2018
Within computer science, an algorithm is a process or set of rules that are followed to solve a problem.
Chris Smith has designed and written a book for beginners who have no previous experience (or are even aware of the concept) with machine learning. For openers he states, ‘Machine learning is a growing field that has gained lots of momentum within the last few years. From new drug discovery to better web search, credit card fraud detection and language translation, it is upending how we learn, interact, and live. Machine learning is a field within computer science that exists to help make artificial intelligence a reality by enabling computers to learn, act and adapt on their own. One of the best examples of this is Google's self-driving car project called Waymo, which began in 2009. From rain storms to traffic jams and road closures, self-driving vehicles need to constantly learn and adapt to changing road conditions, and machine learning is what makes it possible.�
Nice accessible intro but the book rapidly moves into the realm of algorithms or sets of rules.
‘Multiple components work in tandem to enable machines to learn, but one of the most prominent are algorithms. Algorithms - or sets of rules - are what enable computers to work with data and make predictions from it.� He presents three popular algorithms - supervised, unsupervised and semi-supervised. ‘Decision trees fall into "supervised" learning, which is important to understand.�
Moving into the subject of the book, ‘Decision trees are a powerful and versatile tool that can be used to solve a wide-range of problems. They can be quickly scratched out by hand to make a fast decision or coded in an algorithm and used to approach complex scenarios.� Or breaking it down into a definition, ‘At its core, a decision tree is a tool that helps you make better decisions by exploring outcomes and scenarios, but there are many other ways that it can be explained. Here are a few: A decision tree is a graph that uses branching methods to illustrate a course of action and various outcomes. A decision tree is a flowchart that helps you make decisions while taking possible outcomes into consideration. A decision tree helps you assess and analyze scenarios and consequences that you might not normally think of.�
The technical aspects of learning this algorithm Chris handles very well indeed: ‘There are many ways to approach making a decision tree by hand, and one of the easiest is to tackle the problem in 6 steps: Step 1: Determine Your Initial Question/Problem Step 2: Determine Your Decision and Unknown Step 3: Determine the Values Step 4: Determine the Probabilities Step 5: Calculate the Weighted Value Step 6: Calculate the Net Benefit of Each Decision� and then follows this lesson with scenarios that illustrate the concepts. ‘Decision trees are powerful when used on a basic level, but when combined with machine learning they can conquer incredible feats. Companies such as Gerber Products Inc. (baby food) to IBM use decision tree algorithms to help make faster and better decisions, and they are not alone. From banking to manufacturing to agriculture, machine learning is revolutionizing how products are made, customers are acquired, and decisions are implemented.�
All of this may seem daunting to read without the context of the visuals that make learning these algorithms less threatening. Chris Smith has tackled a topic about which we must all become familiar in this current world � and he makes the journey entertaining!
Chris Smith has designed and written a book for beginners who have no previous experience (or are even aware of the concept) with machine learning. For openers he states, ‘Machine learning is a growing field that has gained lots of momentum within the last few years. From new drug discovery to better web search, credit card fraud detection and language translation, it is upending how we learn, interact, and live. Machine learning is a field within computer science that exists to help make artificial intelligence a reality by enabling computers to learn, act and adapt on their own. One of the best examples of this is Google's self-driving car project called Waymo, which began in 2009. From rain storms to traffic jams and road closures, self-driving vehicles need to constantly learn and adapt to changing road conditions, and machine learning is what makes it possible.�
Nice accessible intro but the book rapidly moves into the realm of algorithms or sets of rules.
‘Multiple components work in tandem to enable machines to learn, but one of the most prominent are algorithms. Algorithms - or sets of rules - are what enable computers to work with data and make predictions from it.� He presents three popular algorithms - supervised, unsupervised and semi-supervised. ‘Decision trees fall into "supervised" learning, which is important to understand.�
Moving into the subject of the book, ‘Decision trees are a powerful and versatile tool that can be used to solve a wide-range of problems. They can be quickly scratched out by hand to make a fast decision or coded in an algorithm and used to approach complex scenarios.� Or breaking it down into a definition, ‘At its core, a decision tree is a tool that helps you make better decisions by exploring outcomes and scenarios, but there are many other ways that it can be explained. Here are a few: A decision tree is a graph that uses branching methods to illustrate a course of action and various outcomes. A decision tree is a flowchart that helps you make decisions while taking possible outcomes into consideration. A decision tree helps you assess and analyze scenarios and consequences that you might not normally think of.�
The technical aspects of learning this algorithm Chris handles very well indeed: ‘There are many ways to approach making a decision tree by hand, and one of the easiest is to tackle the problem in 6 steps: Step 1: Determine Your Initial Question/Problem Step 2: Determine Your Decision and Unknown Step 3: Determine the Values Step 4: Determine the Probabilities Step 5: Calculate the Weighted Value Step 6: Calculate the Net Benefit of Each Decision� and then follows this lesson with scenarios that illustrate the concepts. ‘Decision trees are powerful when used on a basic level, but when combined with machine learning they can conquer incredible feats. Companies such as Gerber Products Inc. (baby food) to IBM use decision tree algorithms to help make faster and better decisions, and they are not alone. From banking to manufacturing to agriculture, machine learning is revolutionizing how products are made, customers are acquired, and decisions are implemented.�
All of this may seem daunting to read without the context of the visuals that make learning these algorithms less threatening. Chris Smith has tackled a topic about which we must all become familiar in this current world � and he makes the journey entertaining!
June 24, 2018
As the world continues to diversify technologically, this book continues to gain relevance. This book takes on the two major algorithms decision trees and random forests. The Author Chris Smith describes these two algorithms vividly and throws insight on their strength and weaknesses.
In the past, I have always imagined how self-driving cars manage to find their way. This book has enlightened me so much. It an educational book good for those with interest in machine language and ideas of the future.
Chris Smith is an exceptional writer who understands how to break down ideas and put points forward. The clarity of his ideas and the choice of language makes it easy to understand this book.
He looked at the supervised learning, the unsupervised learning, and semi-supervised learning. I love this book, it’s certainly a good place to start learning.
This book is powerful and touches all spheres of life. From product design to online search. They can be applied to solve various kinds of problems.
I am not so much of a tech Savvy but I can differentiate a good book from the others. This is one book you won’t regret investing in.
In the past, I have always imagined how self-driving cars manage to find their way. This book has enlightened me so much. It an educational book good for those with interest in machine language and ideas of the future.
Chris Smith is an exceptional writer who understands how to break down ideas and put points forward. The clarity of his ideas and the choice of language makes it easy to understand this book.
He looked at the supervised learning, the unsupervised learning, and semi-supervised learning. I love this book, it’s certainly a good place to start learning.
This book is powerful and touches all spheres of life. From product design to online search. They can be applied to solve various kinds of problems.
I am not so much of a tech Savvy but I can differentiate a good book from the others. This is one book you won’t regret investing in.
January 10, 2018
In the book, Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees, the author Chris Smith makes the complicated, simple. Decision tree and random forest algorithms are often used throughout business to more quickly assimilate information and make it more accessible. These algorithms work seamlessly behind the scenes in industries from credit scoring to product development. With the use of easy to understand drawings, Chris Smith puts together a comprehensive look at how to use these applications to your advantage whether in business or even in your personal life. His example of the ice cream shop, and whether or not the shop should move to a different location is a great tutorial that explains in layman's terms how decision trees work at a very fundamental level. A great visual guide with clear illustrations, this book is strongly recommended for the beginner in machine learning algorithms in Python.
February 11, 2018
This is a really excellent guide about decision trees and it shows that the author really knows how to engage his readers and make something fairly abstract and complicated much more approachable.
He does that without dumbing down his subject matter or talking to his readers like they are children, which sometimes I see in various beginners guides about anything. Instead, the author uses real life examples to illustrate his points and he uses plenty of visuals that make things easier to understand. The writing is simple and to the point and there is really nothing in the way of grasping these concepts.
I think that some knowledge of machine learning, or at least interest in it, is helpful to have when starting with this guide, but it isn't necessary. The book hits that sweet spot when it is completely suitable for total newbie and still interesting enough for someone more experienced who, maybe, want to refresh his knowledge or look at things in the other way. Very good book.
He does that without dumbing down his subject matter or talking to his readers like they are children, which sometimes I see in various beginners guides about anything. Instead, the author uses real life examples to illustrate his points and he uses plenty of visuals that make things easier to understand. The writing is simple and to the point and there is really nothing in the way of grasping these concepts.
I think that some knowledge of machine learning, or at least interest in it, is helpful to have when starting with this guide, but it isn't necessary. The book hits that sweet spot when it is completely suitable for total newbie and still interesting enough for someone more experienced who, maybe, want to refresh his knowledge or look at things in the other way. Very good book.
July 25, 2018
Genuinely fascinating, this book breaks down "machine learning" into visuals spread out over 100 images.
Smith takes a concept that can seem overwhelmingly complex to understand, and breaks it down page by page.
He's very clear from the outset that this is a book for total beginners, so if you've got a basic understanding how decision trees work this probably isn't for you, but if like me you're coming from a position of total ignorance then you won't find a better starter kit.
Smith breaks down the various types of algorithms used in helping computers adapt and develop their Artificial Intelligence, and then how you can use these methods by hand to solve a vast array of complex issues, from every day personal life choices to investigations in science and medical fields.
This is something that I had never given the slightest thought to before, and since reading it and learning how to perform these myself I have used a decision tree to make several decisions in my life.
February 12, 2018
Informative and very interesting
Decision Trees and Random Forests is a really interesting and informative book. I’m not sure I’ll ever use the knowledge I gained it it’s pretty neat to have a new understanding of algorithms, and how decision trees work. I liked that this book was written with people like me in mind. While I’m not the type of person to go out and create my own decision trees, I love that After reading this book I could do so.
I mostly read this book just out of curiosity and to learn something that I didn’t know about. But I feel like this book would be useful to anyone who wants to start learning about algorithms and decision trees, or anyone who is like me and just love to learn new things! It opens the door to a deeper understanding of how it all works! I highly recommend it because it was very easy to understand and follow!
Decision Trees and Random Forests is a really interesting and informative book. I’m not sure I’ll ever use the knowledge I gained it it’s pretty neat to have a new understanding of algorithms, and how decision trees work. I liked that this book was written with people like me in mind. While I’m not the type of person to go out and create my own decision trees, I love that After reading this book I could do so.
I mostly read this book just out of curiosity and to learn something that I didn’t know about. But I feel like this book would be useful to anyone who wants to start learning about algorithms and decision trees, or anyone who is like me and just love to learn new things! It opens the door to a deeper understanding of how it all works! I highly recommend it because it was very easy to understand and follow!
February 12, 2018
In school, we never actually used decision trees, so I had to check out this concept. I really wish we would have now because it makes so much more sense to use them than some of the other ways we worked on problems in class. This definitely made math a lot more understandable to me, which is something I can really appreciate. I try to keep up with my skills because of the whole “use it or lose it� principle, so I am glad I now have this method to work with. This book was a breeze to follow, so I’d recommend it to anyone that just wants to improve their skills. As stated in the title this book is directed towards teens, which I think would make it a great option for students studying for the college prep exams. It’s a fast read too, so it isn’t overwhelming, and the author has a great knack for engaging his audience.
February 24, 2018
No matter how hard I try to avoid math at high school and university, numbers and algorithms are everywhere you look, behind every interesting and highly advanced videogame that catch your attention, there are tons of numbers and programs behind it, but finally there is a book that makes me understand all that.
‘Decision trees�, ‘random forests�, there are new concepts presented here and while at first I was a bit skeptical about reading it but still I give it a chance and it was the right choice, everything is defined in simple ways that comes along with a lot of visual examples, it’s amazing how well implemented the visual style is in this book, and how these new terms are well established through the book.
This book can become handy in certain situations and I’m so glad I read it, looking now this book, there is still a lot I need to learn but now everything is crystal clear.
‘Decision trees�, ‘random forests�, there are new concepts presented here and while at first I was a bit skeptical about reading it but still I give it a chance and it was the right choice, everything is defined in simple ways that comes along with a lot of visual examples, it’s amazing how well implemented the visual style is in this book, and how these new terms are well established through the book.
This book can become handy in certain situations and I’m so glad I read it, looking now this book, there is still a lot I need to learn but now everything is crystal clear.
June 7, 2018
I found this to be helpful to my understanding of how decision tree algorithms work. When I bought this I couldn’t have known less about machine learning.
As artificial intelligence is growing more and more powerful and becoming more and more common I’ve grown increasingly interested in this area and this was just the introduction to the field that I needed.
Clearly written throughout, I did need to re-read a few pages but that was only due to the complicated-ness of the subject.
There’s loads of diagrams and drawings through the whole book as well which I found super helpful for describing some of the bits which are just really hard to wrap your head around, like Boostrapping.
All in all, this is simply a great guide for if you're just starting out in the world of machine learning.
As artificial intelligence is growing more and more powerful and becoming more and more common I’ve grown increasingly interested in this area and this was just the introduction to the field that I needed.
Clearly written throughout, I did need to re-read a few pages but that was only due to the complicated-ness of the subject.
There’s loads of diagrams and drawings through the whole book as well which I found super helpful for describing some of the bits which are just really hard to wrap your head around, like Boostrapping.
All in all, this is simply a great guide for if you're just starting out in the world of machine learning.
January 20, 2018
Decision Trees and Random Forests is a guide for beginners. The author provides a great visual exploration to decision tree and random forests. There are common questions on both the topics which readers could solve and know their efficacy and progress. The book teaches you to build decision tree by hand and gives its strengths and weakness. The author also provides introduction to Decision Tree Algorithms, their probable drawbacks and the different ways to build them. There is also introduction to Random Forests such as how it is built and how it predicts.In all the book is great for people with little or no knowledge of the given topics and does a wonderful job in increasing their comprehension and making them decently equipped through visuals and other means.
February 26, 2018
Decision Trees was well-written and easy to follow. As a beginner, I picked up this book because I was curious to see if the writer could explain a concept I knew little to nothing about and teach me something. That is exactly what happened. I learned from reading this book. The writing is done in such a way that made it understandable and reader-friendly. There was no condescending tone or "dumbing down" of the information, which I very much appreciated. I have a better understanding of algorithms. The illustrations/drawings were an added bonus in my opinion, and visual learners can absolutely learn from these. If you run into algorithms in school or math class and want a guide to help you, this book is for you.
May 5, 2018
This book really left me scratching my head -
The first third was SUPER SIMPLE! I mean simple like it what we are taught in fifth grade... your basic tree diagram.
The other (almost) two thirds was what I expected from the title, a very basic introduction to machine learning. I enjoyed thinking about how to make machines think :-)
That missing part of a third? Another major head scratcher - PYTHON
Who cares whether it's good or bad. Who cares whether it's hard or easy to understand. I appreciate what Mr. Smith was trying to do, giving us a step by step, but he is giving web links on a Kindle reader. Web links. If I want to go that route, I'll just hop on my computer to begin with, and google the requisite links. But I don't want to research! I want to read!
The first third was SUPER SIMPLE! I mean simple like it what we are taught in fifth grade... your basic tree diagram.
The other (almost) two thirds was what I expected from the title, a very basic introduction to machine learning. I enjoyed thinking about how to make machines think :-)
That missing part of a third? Another major head scratcher - PYTHON
Who cares whether it's good or bad. Who cares whether it's hard or easy to understand. I appreciate what Mr. Smith was trying to do, giving us a step by step, but he is giving web links on a Kindle reader. Web links. If I want to go that route, I'll just hop on my computer to begin with, and google the requisite links. But I don't want to research! I want to read!
July 9, 2022
It's good for the approach for beginners, but the "visual" aspect leaves much to be desired. The images are mostly inexcusably low quality. The back cover boasts "100+ images that bring concepts and ideas to life", but I that count is only reached through the use of 'images' of data tables and comic-sans text writing out a formula, not any real illustration or diagram. Also, some of the code repositories referenced in the book are no longer available. I'm hesitant to recommend it unless you're truly a beginner into decision trees, random forests, or machine learning in general, and haven't found a good book on the subject.
February 2, 2018
Easy to understand decision tree
Decision tree is a book about building a tree and what it will do for you. The algorithms used are also discussed the author uses easy to follow aids to explain the inner workings of the decision tree. There are qquestions throughout that will help you test your knowledge and understanding of the methods. You will not learn to code from this book but you will get a good understanding of the decision tree and how it functions. Good read with a lot of good information.
Decision tree is a book about building a tree and what it will do for you. The algorithms used are also discussed the author uses easy to follow aids to explain the inner workings of the decision tree. There are qquestions throughout that will help you test your knowledge and understanding of the methods. You will not learn to code from this book but you will get a good understanding of the decision tree and how it functions. Good read with a lot of good information.
February 11, 2018
Complex maths has always been a challenge for me as I just could not relate it to the real world. In “Decision Trees and Random Forests…� abstract concepts are broken down into easily relatable portions. The use of visuals really helped to understand the material with the added bonus that you could go back and review chapters that you might not had completely grasped. A very good tool for the newbie to this concept.
February 26, 2018
Easy Read- A good resource
A simple guide to Decision Trees and Random Forests for beginners or for anyone who wants to refresh the fundamentals, that are used in various industries to make educated, knowledgeable decisions helping reduce risks. The author has used various easy to understand examples, visual layouts to clarify the fundamentals. A great resource for everyone from teenagers to employees and can be read again and again to understand the concepts well.
A simple guide to Decision Trees and Random Forests for beginners or for anyone who wants to refresh the fundamentals, that are used in various industries to make educated, knowledgeable decisions helping reduce risks. The author has used various easy to understand examples, visual layouts to clarify the fundamentals. A great resource for everyone from teenagers to employees and can be read again and again to understand the concepts well.
December 29, 2019
Concise and effective
Not as visual as I expected, but hits all the necessary points in terms of theory and explanation. And it's very concise. Not sure how effective the series would be on more esoteric topics such as deep learning, but for something that's intuitive, such as decision trees, it does the job.
Not as visual as I expected, but hits all the necessary points in terms of theory and explanation. And it's very concise. Not sure how effective the series would be on more esoteric topics such as deep learning, but for something that's intuitive, such as decision trees, it does the job.
November 9, 2024
In Decision Trees and Random Forests, Chris Smith covers the foundations of these machine learning techniques in a way that introduced me to several new concepts. While I found the explanations informative and learned quite a bit, the content sometimes felt a bit technical without always connecting the dots clearly, leaving me wishing for more practical examples to tie it all together.
May 10, 2018
Good primer
Short and concise intro with python code and images, easy read and some insight for when you start your model. But the focus is on the concepts. Also short enough to be used as a reference.
Short and concise intro with python code and images, easy read and some insight for when you start your model. But the focus is on the concepts. Also short enough to be used as a reference.
October 11, 2019
Really helpful at giving a conceptual overview of DT's and RF's in terms a lay person could understand. From this knowledge, went on to use RF in a project. Still a lot to learn but a very helpful book! Thank you Chris Smith!
July 6, 2023
Good writing, lousy hand-made drawings. Sloppy in design, using a invisible thin pencil, making drawings almost unreadable on Kindle. I am somewhat disappointed seeing a good teacher distribute a half-baked product. Since well trained in computer-tools, why not use those to make proper drawings?
June 14, 2018
Неплохое введение в проблематику. Зайдёт менеджерам.
September 13, 2018
Stopped reading when basic math was wrong.
November 30, 2018
Not a bad book about a more obscure corner of machine learning. It's very short, though, and has very little depth. It's also full of typos and formatting issues.
April 17, 2019
Great succinct explanation
I highly recommend this book. The explanations are succinct, clear, and engaging for readers. A few hours will allow you to finish the book.
I highly recommend this book. The explanations are succinct, clear, and engaging for readers. A few hours will allow you to finish the book.
April 24, 2019
This book has some major technical inaccuracies.
October 4, 2021
Very easy way to understand this complex topic.
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July 20, 2022Excellent for novices
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