Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
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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.
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
- 2. What’s in it for 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 vineyard in Alsace, France, the largest wine production region in the world What is a Random Forest?
- 4. Like Raphael, wine makers around the world are facing a grave problem What is a Random Forest?
- 5. So let’s meet with the man himself and try helping him out What is a Random Forest?
- 6. What is a Random Forest?
- 7. What is a Random Forest?
- 8. Hey Raphael. So tell us how’s it going? What is a Random Forest?
- 9. Not as good as before… What is a Random Forest?
- 10. Not many people work in this profession anymore so every stage of production has got slower What is a Random Forest?
- 11. If only we could somehow speed up the process What is a Random Forest?
- 12. Well lucky for you, I’m a machine learning engineer What is a Random Forest?
- 13. How about we automate the quality prediction process for wine? What is a Random Forest?
- 14. Let me begin by telling you what machine learning and random forest is What is a Random Forest?
- 15. Machine learning is the science of applying algorithms that allow a computer to predict the outcome without being explicitly programmed What is a Random Forest?
- 16. • Random forest is an ensemble machine learning algorithm What is a Random Forest?
- 17. • Random forest is an ensemble machine learning algorithm What is a Random Forest?
- 18. • Random forest is an ensemble machine learning algorithm • It operates by building multiple decision trees What is a Random Forest?
- 19. • Random forest is an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: What is a Random Forest?
- 20. • Random forest is an ensemble machine learning algorithm • It operates by building multiple decision trees • They work for both: Classification What is a Random Forest?
- 21. • Random forest is 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 forest is 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 forest is 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 explain random forest in layman terms? How does a Random Forest work?
- 25. Sure! Let’s consider the case of Sam, a high school student How does a Random Forest work?
- 26. Sam is confused on which course to take up Random forest illustration
- 27. He decides to ask a couple of his friends for suggestions Random forest illustration
- 28. Sam approaches Jane first Random forest illustration
- 29. Jane asks Sam a few questions based on which she can suggest Random forest illustration
- 34. Jane forms a decision 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 Random forest illustration
- 36. Theoretical? Calculative? Field of Science? Mathematics Random forest illustration
- 37. Theoretical? Calculative? Yes Mathematics Field of Science? Random forest illustration
- 38. Theoretical? Calculative? Mathematics Field of Science? New in industry? Random forest illustration
- 39. Theoretical? Calculative? Yes Mathematics Field of Science? New in 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 Sam asks Terry for his suggestion Field of Science? New in industry? Random forest illustration
- 42. Theoretical? Calculative? Mathematics Artificial Intelligence Field of Science? New in industry? Scoring? Random forest illustration
- 43. Theoretical? Calculative? Mathematics Artificial Intelligence Scoring? Yes Field of Science? New in industry? Random forest illustration
- 44. Artificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field of Science? New in industry? Random forest illustration
- 45. No Artificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field of Science? New in industry? Random forest illustration
- 46. MathematicsArtificial Intelligence Theoretical? Calculative? Mathematics Scoring? Theoretical? Field of Science? New in industry? Random forest illustration
- 47. Since 2 out of 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 have a number of real world applications already Applications of Random Forest
- 50. In banking, it is used to predict fraudulent customers Applications of Random Forest
- 51. It is used in analysing symptoms of the patients and detecting the disease Applications of Random Forest
- 52. In e–commerce the recommendations are based on customer activity Applications of Random Forest
- 53. Stock market trends can be analysed to predict profit or loss Applications of Random Forest
- 54. To help speed up the process of wine production, we will automate the prediction of wine quality How does a random forest work?
- 55. Suppose, our random forest builds 3 decision trees How does a random forest work?
- 56. Our first decision tree 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 decision tree 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 decision tree 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 unknown glass 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 wine has 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 tree predicts 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 our second 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 tree predicts 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 has a 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 tree predicts 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 out 3 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 our dataset that holds all attribute values required to predict the wine’s quality Use Case: Predicting the quality of wine
- 69. This will be done in RStudio. So let’s begin Use Case: Predicting the quality of wine
- 70. The error rate is 26.81%. Therefore, accuracy is 73.19% (100-26.81) Use Case: Predicting the quality of wine
- 71. So we have automated the process of predicting the quality of wine Use Case: Predicting the quality of wine
- 72. That’s great! Thank you! Use Case: Predicting the quality of wine
- 73. What is a random 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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