Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees

Amazon prints this book locally in their distribution center and is not suitable for a paperback format and reading. This is just a printed form of the kindle format where there are links. Unfortunately a printed book cannot have links! Do not buy paperback version.

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 probability 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!

Whether we would like to acknowledge it or not, algorithms run a significant amount of our daily lives. When we log into social media or scan the news, something is dictating what we do and do not see, informing our opinions in subtle ways and making sure that whatever advertisements we're seeing are meant for our eyes only. These forces are called algorithms, and they are built upon the principles outlined and detailed in this book. This system serves as the foundation of all advertising as we know it today, even when the end result is not us buying something. No matter who you are, if you browse the internet, you are being subjected to the decisions made by algorithms as detailed in this book. While this book is a technical guide through the computer language that has fused itself with our daily lives, anyone can appreciate the insights that this guide has to offer. Even a layperson might benefit from seeing the methodology behind what drives much of the business realm. It is written specifically for beginners, and you don't need any knowledge coming in to appreciate the book. That being said, if you have absolutely no interest in Python or coding language in general, this book probably isn't for you. It is a look into the algorithms that drive our markets, but it is not a theoretical or abstract guide.

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.

I wonder if one day we all start using algorithms for any decision we make in life. We can just have a simplified way to throw all variables in the decision-making mix and assign a task to a machine to perform it for us. At least then we will have machines to blame if the results of a decision don’t turn out as we expect them to! Jokes aside, this book is a pretty simple explanation of machine learning intended for beginners, which you will be able to use not only as a Python developer, but also to understand how trees and forests are used as a metaphor for the logical processes of machine learning.The example for Google driverless vehicles is used to explain how algorithms are used to help the machine analyze the data and come to logical outcomes. If you’ve never really touched upon computer algorithms, you may need to spend some more time in finding the parallel between machine and real learning. However, decision trees and random forests are just complex logical processes - sometimes our brains get into such analysis without even being aware of it. As inconvenient as it may seem, machines are better equipped to solve many problems. The book also goes into the common problems of decision trees and random forests, including practical training examples and visual presentation. It may now be an area for techies, but I think that in a few years, ML will be in the mainstream general knowledge. So. it’s good to get prepared!

I'll not put effort into trying to read a book where relevant parts of it are illegible.eg. P62, calculating entropy, has a equation in a faux-handwritten typeface (err, why?). The letters are about 1mm high.In other places (eg. P34) they show tables of data. I can't read those as they are screenshots, too small and made even less legible by being dithered. Some sufficiently large diagrams are used (eg. P21) again faux handwriting but as black on a dark grey background so they're difficult to make out.Another from the same stable, Neural Networks by M. Taylor which I bought at the same time, doesn't have these faults (though I can't speak to it's pedagogy as I've not read it yet).It's going back.

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.

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.

Don’t be put off if you are not a mathematician or computer programmer, I am certainly not either one. This book is clear and largely visual. I am a great believer in visual learning, a clear diagram can save a hundred words. It is for absolute beginners and the author’s aim is twofold: the first is to enable the reader to understand decision trees and random forests and the second is to enable the reader to create their own, thus simplifying their life.

Don’t expect much more than squinting and confusion from many of the visuals in the paperback, which are often too small to be read accurately, or at all.Many could easily be large enough to be read with ease, without affecting the page layout, as they’re often surrounded by empty white space, characters are frequently illegible and do little more than add unwelcome ambiguity.The rear cover blurb promotes “images that bring concepts to life”, the frontispiece states that the images are formatted for print “so that you don’t have to squint or struggle”.Perhaps the poor execution is a printing/publishing error but the book is irritating to read because of the poor visuals. Text is the reverse however, and had it not been a “visual introduction” it wouldn’t deserve the poor rating.