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Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
The document explains the concept of random forests, an ensemble machine learning algorithm that builds multiple decision trees for classification and regression tasks. It illustrates its application in predicting wine quality by automating the process with various decision trees based on wine attributes. Additionally, real-world applications of random forests in banking, e-commerce, and healthcare are highlighted.
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Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
- 2. What’s in itfor you? What is a Random Forest? Random forest illustration Applications of Random Forest How does a random forest work? Use case: Predicting the quality of wine
- 3. Welcome to Raphael's vineyardin Alsace, France, the largest wine production region in the world What is a Random Forest?
- 4. Like Raphael, winemakers around the world are facing a grave problem What is a Random Forest?
- 5. So let’s meetwith the man himself and try helping him out What is a Random Forest?
- 6. What is aRandom Forest?
- 7. What is aRandom Forest?
- 8. Hey Raphael. Sotell us how’s it going? What is a Random Forest?
- 9. Not as goodas before… What is a Random Forest?
- 10. Not many peoplework in this profession anymore so every stage of production has got slower What is a Random Forest?
- 11. If only wecould somehow speed up the process What is a Random Forest?
- 12. Well lucky foryou, I’m a machine learning engineer What is a Random Forest?
- 13. How about weautomate the quality prediction process for wine? What is a Random Forest?
- 14. Let me beginby telling you what machine learning and random forest is What is a Random Forest?
- 15. Machine learning isthe science of applying algorithms that allow a computer to predict the outcome without being explicitly programmed What is a Random Forest?
- 16. • Random forestis an ensemble machine learning algorithm What is a Random Forest?
- 17. • Random forestis an ensemble machine learning algorithm What is a Random Forest?
- 18. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees What is a Random Forest?
- 19. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: What is a Random Forest?
- 20. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: Classification What is a Random Forest?
- 21. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: Classification What is a Random Forest? Categorising objects based on their attributes
- 22. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: Classification What is a Random Forest? Categorising objects based on their attributes Regression
- 23. • Random forestis an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: Classification What is a Random Forest? Categorising objects based on their attributes Regression Regression problems have continuous or numerical valued output variables
- 24. Could you explainrandom forest in layman terms? How does a Random Forest work?
- 25. Sure! Let’s considerthe case of Sam, a high school student How does a Random Forest work?
- 26. Sam is confusedon which course to take up Random forest illustration
- 27. He decides toask a couple of his friends for suggestions Random forest illustration
- 28. Sam approaches Jane first Randomforest illustration
- 29. Jane asks Sama few questions based on which she can suggest Random forest illustration
- 30.
- 31.
- 32.
- 33.
- 34. Jane forms adecision tree based on Sam’s response and gives her suggestion Theoretical? Calculative? Yes Mathematics Random forest illustration
- 35. Next, Sam approaches Bella Theoretical? Calculative? Mathematics Randomforest illustration
- 36. Theoretical? Calculative? Field of Science? Mathematics Randomforest illustration
- 37. Theoretical? Calculative? Yes Mathematics Field of Science? Randomforest illustration
- 38. Theoretical? Calculative? Mathematics Field of Science? Newin industry? Random forest illustration
- 39. Theoretical? Calculative? Yes Mathematics Field of Science? Newin industry? Random forest illustration
- 40. Similar to Jane,Bella also forms a decision tree based on Sam’s response and gives her suggestion Theoretical? Calculative? Mathematics Artificial Intelligence Field of Science? New in industry? Random forest illustration
- 41. Theoretical? Calculative? Mathematics Artificial Intelligence Samasks Terry for his suggestion Field of Science? New in industry? Random forest illustration
- 42. Theoretical? Calculative? Mathematics Artificial Intelligence Fieldof Science? New in industry? Scoring? Random forest illustration
- 43. Theoretical? Calculative? Mathematics Artificial Intelligence Scoring? Yes Fieldof Science? New in industry? Random forest illustration
- 44. Artificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field ofScience? New in industry? Random forest illustration
- 45. No Artificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field ofScience? New in industry? Random forest illustration
- 46. MathematicsArtificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field ofScience? New in industry? Random forest illustration
- 47. Since 2 outof 3 friends suggested Maths, Sam decides to take Maths Mathematics Random forest illustration
- 48. So, you see,more the number of decision trees, more accurate will be the prediction Applications of Random Forest
- 49. Random forests havea number of real world applications already Applications of Random Forest
- 50. In banking, itis used to predict fraudulent customers Applications of Random Forest
- 51. It is usedin analysing symptoms of the patients and detecting the disease Applications of Random Forest
- 52. In e–commerce the recommendationsare based on customer activity Applications of Random Forest
- 53. Stock market trendscan be analysed to predict profit or loss Applications of Random Forest
- 54. To help speedup the process of wine production, we will automate the prediction of wine quality How does a random forest work?
- 55. Suppose, our randomforest builds 3 decision trees How does a random forest work?
- 56. Our first decisiontree splits based on chlorides and alcohol content Chlorides<0.08 NoYes Quality = LowAlcohol>6 Quality = High Quality = Medium Yes No How does a random forest work?
- 57. Our second decisiontree splits based on pH and sulphate content Sulphates < 0.6 NoYes Quality = LowpH < 3.5 Quality = High Yes No Quality = Medium How does a random forest work?
- 58. Our third decisiontree splits based on sugar content and pH level Sugar < 2.5 NoYes Quality = LowpH < 3.5 Quality = High Yes No Quality = Medium How does a random forest work?
- 59. Here’s an unknownglass of wine. Let’s use our random forest to predict its quality Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 How does a random forest work?
- 60. The new winehas 0.04 chloride content and alcohol content of 5 Chlorides<0.08 NoYes Quality = LowAlcohol>6 Quality = High Quality = Medium Yes No Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 How does a random forest work?
- 61. Our first treepredicts the wine to have a medium quality Chlorides<0.08 NoYes Alcohol>6 Yes No Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 How does a random forest work? Quality = Low Quality = MediumQuality = High
- 62. This is oursecond decision tree. The wine has sulphates content of 0.5 and a pH level of 3.4 Sulphates < 0.6 NoYes Quality = LowpH < 3.5 Quality = High Yes No Quality = Medium Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 How does a random forest work?
- 63. The second treepredicts our wine to be of a high quality Sulphates < 0.6 NoYes pH < 3.5 Quality = High Yes No Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 How does a random forest work? Quality = Low Quality = Medium
- 64. Our wine hasa 1.5 sugar content and as already mentioned, 0.06 chlorides content Sugar < 2.5 NoYes Quality = Low Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 pH < 3.5 Quality = High Yes No Quality = Medium How does a random forest work?
- 65. Our third treepredicts the unknown wine to be of high quality Sugar < 2.5 NoYes Quality = Low Feature Value Chloride content 0.04 Alcohol content 5 Sulphate content 0.5 pH level 3.4 Sugar content 1.5 pH < 3.5 Quality = High Yes No Quality = Medium How does a random forest work?
- 66. Since 2 out3 decisions trees indicates the quality of our wine to be high, the forest predicts the same How does a random forest work?
- 67. Sounds promising! So,shall we begin? How does a random forest work?
- 68. This is ourdataset that holds all attribute values required to predict the wine’s quality Use Case: Predicting the quality of wine
- 69. This will bedone in RStudio. So let’s begin Use Case: Predicting the quality of wine
- 70. The error rateis 26.81%. Therefore, accuracy is 73.19% (100-26.81) Use Case: Predicting the quality of wine
- 71. So we haveautomated the process of predicting the quality of wine Use Case: Predicting the quality of wine
- 72. That’s great! Thankyou! Use Case: Predicting the quality of wine
- 73. What is arandom forest? How does a random forest work? Key Takeaways Predicting quality of wine using R Applications of random forest How does a random forest work? Training a random forest
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