Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees by Chris SmithIf you 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.
Machine Learning using Decision Trees and Random Forests in Python with Code
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Decision trees are a type of model used for both classification and regression. Trees answer sequential questions which send us down a certain route of the tree given the answer. This is easy to see with the image below which maps out whether or not to play golf. The flow of this tree works downward beginning at the top with the outlook. The outlook has one of three options: sunny, overcast, or rainy.
Random forests consist of multiple single trees each based on a random sample of the training data. They are typically more accurate than single decision trees.
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What is a Decision Tree?
Random forests or random decision forests are an ensemble learning method for classification , regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes classification or mean prediction regression of the individual trees. The first algorithm for random decision forests was created by Tin Kam Ho  using the random subspace method ,  which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. An extension of the algorithm was developed by Leo Breiman  and Adele Cutler ,  who registered  "Random Forests" as a trademark as of [update] , owned by Minitab, Inc.
If you find this content useful, please consider supporting the work by buying the book! Previously we have looked in depth at a simple generative classifier naive Bayes; see In Depth: Naive Bayes Classification and a powerful discriminative classifier support vector machines; see In-Depth: Support Vector Machines. Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called random forests. Random forests are an example of an ensemble method, meaning that it relies on aggregating the results of an ensemble of simpler estimators. The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting! We will see examples of this in the following sections. We begin with the standard imports:.