Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn
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The document provides an overview of decision trees, explaining their structure and functionality as algorithms for making decisions. It elaborates on their applications in classification and regression, illustrated with examples like predicting survival on a ship based on various factors. Additionally, the document includes a practical use case in R for survival prediction, demonstrating data handling and classification techniques.
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![How does a Decision Tree work?
-[2/9.log₂(2/9) + 2/9.log₂(2/9) + 1/9.log₂(1/9) + 4/9.log₂(4/9)]=
-∑ⁱₓ₌₁p(valueₓ).log₂(p(valueₓ))
Shape Size Label Number
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![How does a Decision Tree work?
-[2/9.log₂(2/9) + 2/9.log₂(2/9) + 1/9.log₂(1/9) + 4/9.log₂(4/9)]= 1.8282
-∑ⁱₓ₌₁p(valueₓ).log₂(p(valueₓ))
Shape Size Label Number
round 10 ball 2
rectangle 6 book 2
rectangle 4 card 1
rectangle 2 blocks 4
total 9](https://image.slidesharecdn.com/decisiontreeinrdecisiontreealgorithmdatasciencetutorialmachinelearningsimplilearn-181011104342/85/Decision-Tree-In-R-Decision-Tree-Algorithm-Data-Science-Tutorial-Machine-Learning-Simplilearn-32-320.jpg)
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decisiontrees.ppt
decisiontrees.pptLvlShivaNagendra This document provides an introduction to decision trees, which are a type of predictive model that uses a tree-like structure to determine the class of records. Decision trees work by recursively splitting a dataset into purer subsets based on attribute values, resulting in a flowchart-like structure. They have an intuitive appeal as rules can be represented visually or as "if-then" statements. The document discusses key aspects of decision trees such as how they are constructed, evaluated, pruned to prevent overfitting, and their advantages and limitations for classification tasks.
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Understanding Decision Trees in Machine Learning: A Comprehensive Guide
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6/18/25: "For those planning the Training Program Do Welcome. Happy Summer 2k25. You are not ignored and much appreciated. Our updates are ongoing and weekly since Spring. I Hope you Enjoy the Practitioner Grad Level. There's more to come. We will also be wrapping up Level One. So I can work on Levels 2 topics. Please see documents for any news updates. Also visit our websites. Every decade I release a Campus eMap. I will work on that for summer 25. We have 2 old libraries online thats open. https://ldmchapels.weebly.com "
A Safe House,
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By ©YogiGoddess of ©LDMMIA, ©LDMYoga.
♥Teacher Dept: (Rev Dr) Leslie Moore, ND Yoga (Aide/LPN Trained), Metaphysician,
Using Reiki Practitioner/Master Level Trained.
#yogigoddess @YogiGoddessVEVO
♥LDMMIA & Depts: are fusing the fan clubs so do welcome.
We are timeless and a safe haven / Cyber Space. That’s the design of our Fan/Reader/Loyal Blog.♥
LDM HQ Est. in Ann Arbor, MI 2005.
- Moved to Detroit in 2006,
- Expanded online 2007-2024+
- Became a Beatz Studio in 2009 as Yogi Goddess. After our Apple Podcast
- Relocated to Mount Pleasant MI for College The Pandemic Ending.
- Endemic - Present; Moved back to assist Family in Metro Detroit.
Practitioner Student. Level/Session 2
* The Review & Topics:
* All virtual, adult, education students must be over 18 years to attend LDMMIA eClasses and vStudio Thx.
* Please refer to our Free Workshops anytime for review/notes.
*Tech: Products Sold Separately are for Uploading Size Reasons, THX.
MIA TECH: Videos under Copyright including our Music Video for Yogi Goddess, Can only be picked up vs shop downloaded. We are under vydia.com
Pickup will be our Youtube, for unlisted Playlist.
We do have another Vod for Session 2, Level 1.
After that we move on to Session 3.
Levels 1-3 should be done by August to Sept.
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Organization functions as a Studio 1st. We are a Media Co, Private Sector, and Global Listed.
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Our Biz income are Media monetization within The Entertainment genre. This includes the category of Yoga, Reiki, ASMR, and Music Beatz. Any other tips, donations, B2C sales/Student Tuition are extra. The Biz gifts are appreciated. (We have been given a few $K for random emergencies.)
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Rounds: Warm-up, Main Quiz, Visual Round, Connects (optional bonus)
Design: Simple, clear slides with answer explanations included
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This is why students from these 44 institutions have not received National Se...
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2025 June Year 9 Presentation: Subject selection.pptx
2025 June Year 9 Presentation: Subject selection.pptxmansk2 2025 June Year 9 Presentation: Subject selection
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machine Learning |Simplilearn
- 2. What’s in it for you? What is a Decision Tree? What problems can be solved using Decision Trees? How does a Decision Tree work? Use Case: Survival prediction in R
- 3. What is a Decision Tree?
- 4. Left or right? What is a Decision Tree?
- 5. Wait or go? Left or right? What is a Decision Tree?
- 6. Decision Tree is a tree shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction Wait or go? Left or right? What is a Decision Tree?
- 7. What is a Decision Tree? Shopkeeper
- 8. What is a Decision Tree? I must organize my stall.. Shopkeeper
- 9. What is a Decision Tree? Shopkeeper
- 10. What is a Decision Tree? Is it coloured orange? Is it round? No Yes YesNo Shopkeeper
- 11. What problems can be solved using Decision Tree? Classification: Identifying to which set an object belongs Example: Carrot is orange while broccoli is green
- 12. What problems can be solved using Decision Tree? Classification: Identifying to which set an object belongs Example: Carrot is orange while broccoli is green Regression: Regression problems have continuous or numerical valued output variables Example: Predicting the profits of a company
- 13. How does a Decision Tree work? Terms you must know first…..
- 14. How does a Decision Tree work? NODES Each internal node in a decision tree is a test which splits the objects into different categories Is it coloured orange? Is it round? No Yes YesNo Terms you must know first…..
- 15. How does a Decision Tree work? Is it coloured orange? Is it round? No Yes YesNoThis is a node Terms you must know first….. NODES Each internal node in a decision tree is a test which splits the objects into different categories
- 16. How does a Decision Tree work? Is it coloured orange? Is it round? No Yes YesNo ROOT NODE The node at the top of the decision tree is called the Root node Terms you must know first…..
- 17. How does a Decision Tree work? ROOT NODE The node at the top of the decision tree is called the Root node Is it coloured orange? Is it round? No Yes YesNo This is a root node Terms you must know first…..
- 18. How does a Decision Tree work? LEAF NODE Each external node in a decision tree is called the leaf node. The leaf node is the output Is it coloured orange? Is it round? No Yes YesNo Terms you must know first…..
- 19. How does a Decision Tree work? Is it coloured orange? Is it round? No Yes YesNo This is a leaf node LEAF NODE Each external node in a decision tree is called the leaf node. The leaf node is the output Terms you must know first…..
- 20. How does a Decision Tree work? ENTROPY Entropy is a measure of the messiness of your data collection. The messier or more random your data, higher will be the entropy Terms you must know first…..
- 21. How does a Decision Tree work? This collection has high entropy Terms you must know first….. ENTROPY Entropy is a measure of the messiness of your data collection. The messier or more random your data, higher will be the entropy
- 22. How does a Decision Tree work? This collection has high entropy This collection has low entropy Terms you must know first….. ENTROPY Entropy is a measure of the messiness of your data collection. The messier or more random your data, higher will be the entropy
- 23. How does a Decision Tree work? INFORMATION GAIN Information gain is the decrease obtained in entropy by splitting the data set based on some condition Is it coloured orange? Is it round? No Yes YesNo E1 E2 Terms you must know first…..
- 24. How does a Decision Tree work? INFORMATION GAIN Information gain is the decrease obtained in entropy by splitting the data set based on some condition Is it coloured orange? Is it round? No Yes YesNo E1 E2 E1>E2 INFORMATION GAIN=E1-E2 Terms you must know first…..
- 25. How does a Decision Tree work? Hi, my cupboard is a mess. I must organize my stuff..
- 26. How does a Decision Tree work? Classify the objects based on their attribute set using decision trees
- 27. How does a Decision Tree work? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Let’s look at the attributes for each object
- 28. How does a Decision Tree work? • We split at each level based on certain conditions on the attributes Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 29. How does a Decision Tree work? • We split at each level based on certain conditions on the attributes • Splitting aims at reducing the entropy Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 30. How does a Decision Tree work? • We split at each level based on certain conditions on the attributes • Splitting aims at reducing the entropy -∑ⁱₓ₌₁p(valueₓ).log₂(p(valueₓ)) Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 31. How does a Decision Tree work? -[2/9.log₂(2/9) + 2/9.log₂(2/9) + 1/9.log₂(1/9) + 4/9.log₂(4/9)]= -∑ⁱₓ₌₁p(valueₓ).log₂(p(valueₓ)) Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 32. How does a Decision Tree work? -[2/9.log₂(2/9) + 2/9.log₂(2/9) + 1/9.log₂(1/9) + 4/9.log₂(4/9)]= 1.8282 -∑ⁱₓ₌₁p(valueₓ).log₂(p(valueₓ)) Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 33. How does a Decision Tree work? Now we must find the conditions for our split. Every split must give us the maximum achievable information gain Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9
- 34. How does a Decision Tree work? Our first split will be on shape as that will directly segregate the balls Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 E1 = 1.8282
- 35. How does a Decision Tree work? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle? E2 = 1.3784 E1 = 1.8282
- 36. How does a Decision Tree work? Our second split will be on size as that will directly segregate the books Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle? E2 = 1.3784 E1 = 1.8282
- 37. How does a Decision Tree work? Shape == Rectangle? Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 E3 = 0.716 E2 = 1.3784 E1 = 1.8282
- 38. How does a Decision Tree work? Our third split will once again be on size Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle? E3 = 0.716 E2 = 1.3784 E1 = 1.8282
- 39. How does a Decision Tree work? Size>3? Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle? E3 = 0.716 E2 = 1.3784 E1 = 1.8282 E4 = 0
- 40. How does a Decision Tree work? Size>3? Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle? E2 = 1.3784 E3 = 0.716 E1 = 1.8282 E4 = 0
- 41. How does a Decision Tree work? All our objects are now classified with 100% accuracy Size>3? Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle?
- 42. How does a Decision Tree work? All our objects are now classified with 100% accuracy Size>3? Size>5? Shape Size Label Number round 10 ball 2 rectangle 6 book 2 rectangle 4 card 1 rectangle 2 blocks 4 total 9 Shape == Rectangle?
- 43. Use Case: Survival prediction in R Let’s implement classification of a data set based on Information Gain
- 44. Let’s implement classification of a data set based on Information Gain This is the ID3 algorithm Use Case: Survival prediction in R
- 45. Let’s implement classification of a data set based on Information Gain This is the ID3 algorithm We will be using the RStudio IDE Use Case: Survival prediction in R
- 46. • A ship had 20 lifeboats Use Case: Survival prediction in R
- 47. • A ship had 20 lifeboats • The lifeboats were distributed based on the class, gender and age of the passengers Use Case: Survival prediction in R
- 48. • A ship had 20 lifeboats • The lifeboats were distributed based on the class, gender and age of the passengers • We will develop a model that recognises the relationship between these factors and predicts the survival of a passenger accordingly Use Case: Survival prediction in R
- 49. Use Case: Survival prediction in R We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not
- 50. 1 indicates the person survived the wreck We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not Use Case: Survival prediction in R
- 51. The luxury class of the cabin Use Case: Survival prediction in R We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not
- 52. Numbers of siblings on board Use Case: Survival prediction in R We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not
- 53. Numbers of parents on board Use Case: Survival prediction in R We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not
- 54. Disembark location Use Case: Survival prediction in R We will be using a data set which specifies if a passenger on a ship survived it’s wreck or not
- 55. What is DecisionTree? Problems solved using DecisionTrees How does a decision tree work? Key Takeaways Predicting survivors using R Determining accuracy of prediction Terms to know
- 56. So what’s your next step?
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