[[ Free Reading ]] Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners Author Scott Hartshorn – Andy-palmer.co.uk

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners Machine Learning Made Easy To UnderstandIf You Are Looking For A Book To Help You Understand How The Machine Learning Algorithms Random Forest And Decision Trees Work Behind The Scenes, Then This Is A Good Book For You Those Two Algorithms Are Commonly Used In A Variety Of Applications Including Big Data Analysis For Industry And Data Analysis Competitions Like You Would Find On KaggleThis Book Explains How Decision Trees Work And How They Can Be Combined Into A Random Forest To Reduce Many Of The Common Problems With Decision Trees, Such As Overfitting The Training DataSeveral Dozen Visual ExamplesEquations Are Great For Really Understanding Every Last Detail Of An Algorithm But To Get A Basic Idea Of How Something Works, In A Way That Will Stick With You Months Later, Nothing Beats Pictures This Book Contains Several Dozen Images Which Detail Things Such As How A Decision Tree Picks What Splits It Will Make, How A Decision Tree Can Over Fit Its Data, And How Multiple Decision Trees Can Be Combined To Form A Random ForestThis Is Not A TextbookMost Books, And Other Information On Machine Learning, That I Have Seen Fall Into One Of Two Categories, They Are Either Textbooks That Explain An Algorithm In A Way Similar To And Then The Algorithm Optimizes This Loss Function Or They Focus Entirely On How To Set Up Code To Use The Algorithm And How To Tune The ParametersThis Book Takes A Different Approach That Is Based On Providing Simple Examples Of How Decision Trees And Random Forests Work, And Building On Those Examples Step By Step To Encompass Thecomplicated Parts Of The Algorithms The Actual Equations Behind Decision Trees And Random Forests Get Explained By Breaking Them Down And Showing What Each Part Of The Equation Does, And How It Affects The Examples In QuestionPython Files Things Like Error Checking Or Complicated Conditionals Are Hard To Replicate Outside Of Code However Some Topics Work Quite Well In A Spreadsheet Topics Such As Entropy And Information Gain, Which Is How A Decision Tree Picks Its Splits, Can Be Easily Calculated In A Spreadsheet The Spreadsheet Used To Generate Many Of The Examples In This Book Is Available For Free Download, As Are All Of The Python Scripts That Ran The Random Forests If You Are Someone Who Learns By Playing With The Code, And Editing The Data Or Equations To See What Changes, Then Use Those Resources Along With The Book For A Deeper UnderstandingTopics CoveredThe Topics Covered In This Book AreAn Overview Of Decision Trees And Random ForestsA Manual Example Of How A Human Would Classify A Dataset, Compared To How A Decision Tree Would WorkHow A Decision Tree Works, And Why It Is Prone To OverfittingHow Decision Trees Get Combined To Form A Random ForestHow To Use That Random Forest To Classify Data And Make PredictionsHow To Determine How Many Trees To Use In A Random ForestJust Where Does The Randomness Come FromOut Of Bag Errors Cross Validation How Good Of A Fit Did The Machine Learning Algorithm Make Gini Criteria Entropy Criteria How To Tell Which Split On A Decision Tree Is Best Among Many Possible ChoicesAnd MoreIf You Want To Knowabout How These Machine Learning Algorithms Work, But Don T Need To Reinvent Them, This Is A Good Book For You

About the Author: Scott Hartshorn

Scott Hartshorn is an engineer who analyzes the dynamics of jet engines He is a Master on the data analysis competition site Kaggle, and has used that expertise to write over a dozen books on statistics and data analysis.As an engineer, Scott takes a different approach to writing about data than you usually see Each book is focused on solving a specific problem or type of analysis The books are focused on helping you get to the Eureka moment of deep and lasting understanding, instead of memorizing a bunch of equations.Visit him at www.FairlyNerdy.com

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