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Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka

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The document outlines Edureka's data science certification training, focusing on decision trees and their application in machine learning for classification tasks. It explains the components of decision trees, their working mechanism, and the use of attributes for predictions in various domains like banking and medicine. Additionally, it details how to implement decision trees in R for predicting outcomes such as diabetes susceptibility.

Data & Analytics
Related topics:
Big Data Analytics • Data Analysis Insights • Data Mining Insights • Data Science Insights • Data Scientist • Data Visualization Techniques • Decision Tree Models • Data Analytics Insights • Business Analytics • R Programming Overview
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www.edureka.co/data-scienceEdureka’s Data Science Certification Training Decision Tree
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Will You Learn Today? ClassificationMachine Learning Types Of Classifiers What Is Decision Tree? How Decision Tree Works? Demo In R: Diabetes Prevention Use Case 1 2 3 4 65
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Introduction To Machine Learning Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Training Data Learn Algorithm Build Model Perform Feedback
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning - Example  Amazon has huge amount of consumer purchasing data.  The data consists of consumer demographics (age, sex, location), purchasing history, past browsing history.  Based on this data, Amazon segments its customers, draws a pattern and recommends the right product to the right customer at the right time.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Introduction To Classification  Classification is the problem of identifying to which set of categories a new observation belongs.  It is a supervised learning model as the classifier already has a set of classified examples and from these examples, the classifier learns to assign unseen new examples.  Example: Assigning a given email into "spam" or "non-spam" category. Is this A or B ?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification - Example Feed the classifier with training data set and predefined labels. It will learn to categorize particular data under a specific label. How to train my model to identify spam mails from genuine mails? Source IP Address Phrases in the text Subject Line HTML Tags
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification Use Cases Banking Remote sensing Medicine Banking Identification of loan risk applicants by their probability of defaulting payments. Medicine Identification of at-risk patients and disease trends. Remote sensing Identification of areas of similar land use in a GIS database. Marketing Identifying customer churn. Use-cases Marketing
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Types Of Classifiers Decision Tree • Decision tree builds classification models in the form of a tree structure. • It breaks down a dataset into smaller and smaller subsets. • Random Forest is an ensemble classifier made using many decision tree models. • Ensemble models combine the results from different models. Random Forest Naïve Bayes • It is a classification technique based on Bayes' Theorem with an assumption of independence among attributes.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Decision Tree?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Decision Tree?  A decision tree uses a tree structure to specify sequences of decisions and consequences.  A decision tree employs a structure of nodes and branches.  The depth of a node is the minimum number of steps required to reach the node from the root.  Eventually, a final point is reached and a prediction is made. Gender AgeIncome Yes No Yes No Root Node Internal Node Branch NodeDepth=1 Female Male <=40 >40 Leaf Node <=45000 >45000
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Use Case - Credit Risk Detection  To minimize loss, the bank needs a decision rule to predict whom to give approval of the loan.  An applicant’s demographic (income, debts, credit history) and socio-economic profiles are considered.  Data science can help banks recognize behavior patterns and provide a complete view of individual customers.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? Let’s take an example, We have taken dataset consisting of: • Weather information of last 14 days • Whether match was played or not on that particular day Now using the decision tree we need to predict whether the game will happen if the weather condition is Outlook = Rain Humidity = High Wind = Weak Play = ?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? From our data, we will choose one variable “Outlook” and will see how it affects the variable “Play” Day Outlook Humidity Wind Play D1 Sunny High Weak No D2 Sunny High Strong No D3 Overcast High Weak Yes D4 Rain High Weak Yes D5 Rain Normal Weak Yes D6 Rain Normal Strong No D7 Overcast Normal Strong Yes D8 Sunny High Weak No D9 Sunny Normal Weak Yes D10 Rain Normal Weak Yes D11 Sunny Normal Strong Yes D12 Overcast High Strong Yes D13 Overcast Normal Weak Yes D14 Rain High Strong No Outlook Play: 9 Yes, 5 No Sunny Overcast Rain There are 3 types of Outlook Here
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We can further divide our data based on Outlook. Outlook Overcast Sunny Rain Day Outlook Humidity Wind D1 Sunny High Weak D2 Sunny High Strong D8 Sunny High Weak D9 Sunny Normal Weak D11 Sunny Normal Strong 2 Yes / 3 No Split further Pure subset Will play 3 Yes / 2 No Split further Day Outlook Humidity Wind D4 Rain High Weak D5 Rain Normal Weak D6 Rain Normal Strong D10 Rain Normal Weak D14 Rain High Strong We will split the data until we get pure subsets at every branch 9 Yes / 5 No Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Day Humidity Wind D1 High Weak D2 High Strong D8 High Weak Pure subset Day Humidity Wind D9 Normal Weak D11 Normal Strong Pure subset 3 Yes / 2 No Split further Day Outlook Humidity Wind D4 Rain High Weak D5 Rain Normal Weak D6 Rain Normal Strong D10 Rain Normal Weak D14 Rain High Strong Pure subset Will play Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Weak Strong Wind Will play Will not play Day Humidity Wind D1 High Weak D2 High Strong D8 High Weak Pure subset Day Humidity Wind D9 Normal Weak D11 Normal Strong Pure subset Day Humidity Wind D4 High Weak D5 Normal Weak D10 Normal Weak Pure subset Day Humidity Wind D6 Normal Strong D14 High Strong Pure subset Pure subset Will play Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Weak Strong Wind Will play Will not play Will play
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How will I know which attribute to take? I’ll show you how
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem – Client Subscription Consider the case of a bank that wants to market its products to the appropriate customers. Given the demographics of clients and their reactions to previous campaign phone calls, the bank's goal is to predict which clients would subscribe. The attributes are: • Job • Marital status • Education • Housing • Loan • Contact • Poutcome
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute?  A common way to identify the most informative attribute is to use entropy-based methods.  The entropy methods select the most informative attribute.  Entropy (H) can be calculated as, x = Datapoint p(x) = Probability of x H = Entropy of x
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Now, let’s do some mathematics on it  Therefore, the root is only 10.55% pure on the subscribed = yes class.  Conversely, it is 89.45% pure on the subscribed = no class. P(subscribed=yes) = 0.1055 P(subscribed=no) = 0.8945 Hsubscribed = −0.1055·log20.1055–0.8945·log20.8945 ≈ 0.4862 P(subscribed = yes) = 1-1789/2000 =10.55% Let’s say, the overall fraction of the clients who have not subscribed to is 1,789 out of the total population of 2,000.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Conditional entropy is, Hsubscribed|contact = 0.4661 Calculating conditional entropy for subscribed|contact gives us following result.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute?  The information gain of an attribute A is defined as the difference between the base entropy (HS) and the conditional entropy of the attribute (HS|A).  Attribute poutcome has the most information gain and is the most informative variable. Therefore, poutcome is chosen for the first split of the decision tree. InfoGainA = HS – HS|A
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Finally, we get the following decision tree Poutcome EducationNo Job Yes Root Node Branch Node Failure, Other, Unknown Secondary, tertiary Success Internal Node Primary, Unknown Leaf Node Admin, blue-collar, management, technician Self-employed, student, unemployed No Yes
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Decision Tree - Pros And Cons
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it? Sure! Let me take you through the steps to predict the vulnerable patients.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation Doctor gets the following data from the medical history of the patient.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation We will divide our entire dataset into two subsets as: • Training dataset -> to train the model • Testing dataset -> to validate and make predictions
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  Here, we implement decision tree in R using following commands.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  We get the output as follows but this is not easy to understand, so let’s visualize it for better understanding.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation For plotting we can use the following commands > plot(diabet_model,margin = 0.1) > text(diabet_model,use.n= TRUE,pretty = TRUE,cex =0.6) glucose_conc< 154.5 Diabetes_pedigree_fn<0.315glucose_conc< 131 blood_pressure>=72 NO 68/18 NO 12/3 YES 5/11 glucose_conc< 100.5 NO 107/3 BMI <26.35 Age >=53.5 NO 6/4 YES 9/65 NO 93/13 Age <30.5 Age >=53.5 NO 5/2 YES 13/39 NO 35/18
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation Now, we can use our model to predict the output of our testing dataset. We can use the following code for predicting the output.  pred_diabet<-predict(diabet_model,newdata = diabet_test,type ="class")  pred_diabet
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation We get the following output for our testing dataset where: “YES” means the probability of patient being vulnerable to diabetes is positive “NO” means the probability of patient being vulnerable to diabetes is negative.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  library(caret)  confusionMatrix(table(pred_diabet,diabet_test$is_diabetic)) We can create confusion matrix for the model using the library caret to know how good is our model.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data acquisition Divide dataset Implement model Visualize Accuracy = 71.13% The accuracy (or the overall success rate) is a metric defining the rate at which a model has classified the records correctly. A good model should have a high accuracy score. Data Acquisition Divide dataset Implement model Visualize Model Validation
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Course Details Go to www.edureka.co/data-science Get Edureka Certified in Data Science Today! What our learners have to say about us! Shravan Reddy says- “I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well.. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop”. Gnana Sekhar says - “Edureka Data science course provided me a very good mixture of theoretical and practical training. LMS pre recorded sessions and assignments were very good as there is a lot of information in them that will help me in my job. Edureka is my teaching GURU now...Thanks EDUREKA.” Balu Samaga says - “It was a great experience to undergo and get certified in the Data Science course from Edureka. Quality of the training materials, assignments, project, support and other infrastructures are a top notch.”
www.edureka.co/data-scienceEdureka’s Data Science Certification Training

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Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka

  • 1. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Decision Tree
  • 2. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Will You Learn Today? ClassificationMachine Learning Types Of Classifiers What Is Decision Tree? How Decision Tree Works? Demo In R: Diabetes Prevention Use Case 1 2 3 4 65
  • 3. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning
  • 4. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Introduction To Machine Learning Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Training Data Learn Algorithm Build Model Perform Feedback
  • 5. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning - Example  Amazon has huge amount of consumer purchasing data.  The data consists of consumer demographics (age, sex, location), purchasing history, past browsing history.  Based on this data, Amazon segments its customers, draws a pattern and recommends the right product to the right customer at the right time.
  • 6. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification
  • 7. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Introduction To Classification  Classification is the problem of identifying to which set of categories a new observation belongs.  It is a supervised learning model as the classifier already has a set of classified examples and from these examples, the classifier learns to assign unseen new examples.  Example: Assigning a given email into "spam" or "non-spam" category. Is this A or B ?
  • 8. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification - Example Feed the classifier with training data set and predefined labels. It will learn to categorize particular data under a specific label. How to train my model to identify spam mails from genuine mails? Source IP Address Phrases in the text Subject Line HTML Tags
  • 9. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification Use Cases Banking Remote sensing Medicine Banking Identification of loan risk applicants by their probability of defaulting payments. Medicine Identification of at-risk patients and disease trends. Remote sensing Identification of areas of similar land use in a GIS database. Marketing Identifying customer churn. Use-cases Marketing
  • 10. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Types Of Classifiers Decision Tree • Decision tree builds classification models in the form of a tree structure. • It breaks down a dataset into smaller and smaller subsets. • Random Forest is an ensemble classifier made using many decision tree models. • Ensemble models combine the results from different models. Random Forest Naïve Bayes • It is a classification technique based on Bayes' Theorem with an assumption of independence among attributes.
  • 11. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Decision Tree?
  • 12. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Decision Tree?  A decision tree uses a tree structure to specify sequences of decisions and consequences.  A decision tree employs a structure of nodes and branches.  The depth of a node is the minimum number of steps required to reach the node from the root.  Eventually, a final point is reached and a prediction is made. Gender AgeIncome Yes No Yes No Root Node Internal Node Branch NodeDepth=1 Female Male <=40 >40 Leaf Node <=45000 >45000
  • 13. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Use Case - Credit Risk Detection  To minimize loss, the bank needs a decision rule to predict whom to give approval of the loan.  An applicant’s demographic (income, debts, credit history) and socio-economic profiles are considered.  Data science can help banks recognize behavior patterns and provide a complete view of individual customers.
  • 14. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works?
  • 15. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? Let’s take an example, We have taken dataset consisting of: • Weather information of last 14 days • Whether match was played or not on that particular day Now using the decision tree we need to predict whether the game will happen if the weather condition is Outlook = Rain Humidity = High Wind = Weak Play = ?
  • 16. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? From our data, we will choose one variable “Outlook” and will see how it affects the variable “Play” Day Outlook Humidity Wind Play D1 Sunny High Weak No D2 Sunny High Strong No D3 Overcast High Weak Yes D4 Rain High Weak Yes D5 Rain Normal Weak Yes D6 Rain Normal Strong No D7 Overcast Normal Strong Yes D8 Sunny High Weak No D9 Sunny Normal Weak Yes D10 Rain Normal Weak Yes D11 Sunny Normal Strong Yes D12 Overcast High Strong Yes D13 Overcast Normal Weak Yes D14 Rain High Strong No Outlook Play: 9 Yes, 5 No Sunny Overcast Rain There are 3 types of Outlook Here
  • 17. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We can further divide our data based on Outlook. Outlook Overcast Sunny Rain Day Outlook Humidity Wind D1 Sunny High Weak D2 Sunny High Strong D8 Sunny High Weak D9 Sunny Normal Weak D11 Sunny Normal Strong 2 Yes / 3 No Split further Pure subset Will play 3 Yes / 2 No Split further Day Outlook Humidity Wind D4 Rain High Weak D5 Rain Normal Weak D6 Rain Normal Strong D10 Rain Normal Weak D14 Rain High Strong We will split the data until we get pure subsets at every branch 9 Yes / 5 No Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
  • 18. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Day Humidity Wind D1 High Weak D2 High Strong D8 High Weak Pure subset Day Humidity Wind D9 Normal Weak D11 Normal Strong Pure subset 3 Yes / 2 No Split further Day Outlook Humidity Wind D4 Rain High Weak D5 Rain Normal Weak D6 Rain Normal Strong D10 Rain Normal Weak D14 Rain High Strong Pure subset Will play Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
  • 19. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Weak Strong Wind Will play Will not play Day Humidity Wind D1 High Weak D2 High Strong D8 High Weak Pure subset Day Humidity Wind D9 Normal Weak D11 Normal Strong Pure subset Day Humidity Wind D4 High Weak D5 Normal Weak D10 Normal Weak Pure subset Day Humidity Wind D6 Normal Strong D14 High Strong Pure subset Pure subset Will play Day Outlook Humidity Wind D3 Overcast High Weak D7 Overcast Normal Strong D12 Overcast High Strong D13 Overcast Normal Weak
  • 20. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Decision Tree Works? We will use Humidity column to split the subset “Sunny” further. Will playWill not play Outlook Overcast Sunny Rain Humidity NormalHigh Weak Strong Wind Will play Will not play Will play
  • 21. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How will I know which attribute to take? I’ll show you how
  • 22. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Problem – Client Subscription Consider the case of a bank that wants to market its products to the appropriate customers. Given the demographics of clients and their reactions to previous campaign phone calls, the bank's goal is to predict which clients would subscribe. The attributes are: • Job • Marital status • Education • Housing • Loan • Contact • Poutcome
  • 23. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute?  A common way to identify the most informative attribute is to use entropy-based methods.  The entropy methods select the most informative attribute.  Entropy (H) can be calculated as, x = Datapoint p(x) = Probability of x H = Entropy of x
  • 24. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Now, let’s do some mathematics on it  Therefore, the root is only 10.55% pure on the subscribed = yes class.  Conversely, it is 89.45% pure on the subscribed = no class. P(subscribed=yes) = 0.1055 P(subscribed=no) = 0.8945 Hsubscribed = −0.1055·log20.1055–0.8945·log20.8945 ≈ 0.4862 P(subscribed = yes) = 1-1789/2000 =10.55% Let’s say, the overall fraction of the clients who have not subscribed to is 1,789 out of the total population of 2,000.
  • 25. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Conditional entropy is, Hsubscribed|contact = 0.4661 Calculating conditional entropy for subscribed|contact gives us following result.
  • 26. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute?  The information gain of an attribute A is defined as the difference between the base entropy (HS) and the conditional entropy of the attribute (HS|A).  Attribute poutcome has the most information gain and is the most informative variable. Therefore, poutcome is chosen for the first split of the decision tree. InfoGainA = HS – HS|A
  • 27. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Choose An Attribute? Finally, we get the following decision tree Poutcome EducationNo Job Yes Root Node Branch Node Failure, Other, Unknown Secondary, tertiary Success Internal Node Primary, Unknown Leaf Node Admin, blue-collar, management, technician Self-employed, student, unemployed No Yes
  • 28. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Decision Tree - Pros And Cons
  • 30. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it? Sure! Let me take you through the steps to predict the vulnerable patients.
  • 31. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation Doctor gets the following data from the medical history of the patient.
  • 32. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation We will divide our entire dataset into two subsets as: • Training dataset -> to train the model • Testing dataset -> to validate and make predictions
  • 33. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  Here, we implement decision tree in R using following commands.
  • 34. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  We get the output as follows but this is not easy to understand, so let’s visualize it for better understanding.
  • 35. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation For plotting we can use the following commands > plot(diabet_model,margin = 0.1) > text(diabet_model,use.n= TRUE,pretty = TRUE,cex =0.6) glucose_conc< 154.5 Diabetes_pedigree_fn<0.315glucose_conc< 131 blood_pressure>=72 NO 68/18 NO 12/3 YES 5/11 glucose_conc< 100.5 NO 107/3 BMI <26.35 Age >=53.5 NO 6/4 YES 9/65 NO 93/13 Age <30.5 Age >=53.5 NO 5/2 YES 13/39 NO 35/18
  • 36. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation Now, we can use our model to predict the output of our testing dataset. We can use the following code for predicting the output.  pred_diabet<-predict(diabet_model,newdata = diabet_test,type ="class")  pred_diabet
  • 37. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation We get the following output for our testing dataset where: “YES” means the probability of patient being vulnerable to diabetes is positive “NO” means the probability of patient being vulnerable to diabetes is negative.
  • 38. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data Acquisition Divide dataset Implement model Visualize Model Validation  library(caret)  confusionMatrix(table(pred_diabet,diabet_test$is_diabetic)) We can create confusion matrix for the model using the library caret to know how good is our model.
  • 39. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo Data acquisition Divide dataset Implement model Visualize Accuracy = 71.13% The accuracy (or the overall success rate) is a metric defining the rate at which a model has classified the records correctly. A good model should have a high accuracy score. Data Acquisition Divide dataset Implement model Visualize Model Validation
  • 40. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Course Details Go to www.edureka.co/data-science Get Edureka Certified in Data Science Today! What our learners have to say about us! Shravan Reddy says- “I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well.. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop”. Gnana Sekhar says - “Edureka Data science course provided me a very good mixture of theoretical and practical training. LMS pre recorded sessions and assignments were very good as there is a lot of information in them that will help me in my job. Edureka is my teaching GURU now...Thanks EDUREKA.” Balu Samaga says - “It was a great experience to undergo and get certified in the Data Science course from Edureka. Quality of the training materials, assignments, project, support and other infrastructures are a top notch.”

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