There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...Simplilearn
This presentation about Random Forest in R will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R presentation :
1. What is Random Forest?
2. How does a Random Forest work?
3. Applications of Random Forest
4. Use case: Predicting the quality of the wine
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbour recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...Simplilearn
This presentation about Decision Tree Tutorial will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a 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. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...Simplilearn
This presentation about Random Forest in R will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R presentation :
1. What is Random Forest?
2. How does a Random Forest work?
3. Applications of Random Forest
4. Use case: Predicting the quality of the wine
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbour recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...Simplilearn
This presentation about Decision Tree Tutorial will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a 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. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R presentation :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
This presentation teaches the concept of Statistical Decision Theory.
Details of this is given here: http://kindsonthegenius.blogspot.hu/2017/12/basics-of-decision-theory.html
Watch the Video here: https://youtu.be/HSc31v67590
Table of Content
What is decision theory?
Application of Decision Theory – Cancer Diagnosis
Formal definition
False positives/False negatives
Minimizing misclassification
Reducing Expected Loss
Introduction to ROC
This is the most simplest and easy to understand ppt. Here you can define what is decision tree,information gain,gini impurity,steps for making decision tree there pros and cons etc which will helps you to easy understand and represent it.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
This presentation teaches the concept of Statistical Decision Theory.
Details of this is given here: http://kindsonthegenius.blogspot.hu/2017/12/basics-of-decision-theory.html
Watch the Video here: https://youtu.be/HSc31v67590
Table of Content
What is decision theory?
Application of Decision Theory – Cancer Diagnosis
Formal definition
False positives/False negatives
Minimizing misclassification
Reducing Expected Loss
Introduction to ROC
This is the most simplest and easy to understand ppt. Here you can define what is decision tree,information gain,gini impurity,steps for making decision tree there pros and cons etc which will helps you to easy understand and represent it.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
"Multilayer perceptron (MLP) is a technique of feed
forward artificial neural network using back
propagation learning method to classify the target
variable used for supervised learning. It consists of multiple layers and non-linear activation allowing it to distinguish data that is not linearly separable."
Credit Card Marketing Classification Trees Fr.docxShiraPrater50
Credit Card Marketing
Classification Trees
From Building Better Models with JMP® Pro,
Chapter 6, SAS Press (2015). Grayson, Gardner
and Stephens.
Used with permission. For additional information,
see community.jmp.com/docs/DOC-7562.
2
Credit Card Marketing
Classification Trees
Key ideas: Classification trees, validation, confusion matrix, misclassification, leaf report, ROC
curves, lift curves.
Background
A bank would like to understand the demographics and other characteristics associated with whether a
customer accepts a credit card offer. Observational data is somewhat limited for this kind of problem, in
that often the company sees only those who respond to an offer. To get around this, the bank designs a
focused marketing study, with 18,000 current bank customers. This focused approach allows the bank to
know who does and does not respond to the offer, and to use existing demographic data that is already
available on each customer.
The designed approach also allows the bank to control for other potentially important factors so that the
offer combination isn’t confused or confounded with the demographic factors. Because of the size of the
data and the possibility that there are complex relationships between the response and the studied
factors, a decision tree is used to find out if there is a smaller subset of factors that may be more
important and that warrant further analysis and study.
The Task
We want to build a model that will provide insight into why some bank customers accept credit card offers.
Because the response is categorical (either Yes or No) and we have a large number of potential predictor
variables, we use the Partition platform to build a classification tree for Offer Accepted. We are primarily
interested in understanding characteristics of customers who have accepted an offer, so the resulting
model will be exploratory in nature.1
The Data Credit Card Marketing BBM.jmp
The data set consists of information on the 18,000 current bank customers in the study.
Customer Number: A sequential number assigned to the customers (this column is hidden and
excluded – this unique identifier will not be used directly).
Offer Accepted: Did the customer accept (Yes) or reject (No) the offer.
Reward: The type of reward program offered for the card.
Mailer Type: Letter or postcard.
Income Level: Low, Medium or High.
# Bank Accounts Open: How many non-credit-card accounts are held by the customer.
1 In exploratory modeling, the goal is to understand the variables or characteristics that drive behaviors or particular outcomes. In
predictive modeling, the goal is to accurately predict new observations and future behaviors, given the current information and
situation.
3
Overdraft Protection: Does the customer have overdraft protection on their checking account(s)
(Yes or No).
Credit Rating: Low, Medium or High.
# Credit Cards Held: The number of cred ...
3. Secondary Data, Online Information Databases, and Measurement.docxtamicawaysmith
3. Secondary Data, Online Information Databases, and Measurement Scaling
1
Primary Scales of Measurement
7
3
8
Scale
Nominal Numbers
Assigned
to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to Finish, in
Seconds
Third
place
Second
place
First
place
Finish
Finish
8.2
9.1
9.6
15.2
14.1
13.4
Primary Scales of Measurement
Nominal Scale: The numbers serve only as labels or tags for identifying and classifying objects.
Ordinal Scale: A ranking scale
Interval Scale: Numerically equal distances on the scale represent equal values in the characteristic being measured.
Ratio Scale: Possesses all the properties of the nominal, ordinal, and interval scales. It has an absolute zero point.
Illustration of Scales of Measurement
Nominal Ordinal Ratio
Scale Scale Scale
Preference $ spent last No. Store Rankings 3 months
1. Parisian
2. Macy’s
3. Kmart
4. Kohl’s
5. J.C. Penney
6. Neiman Marcus
7. Marshalls
8. Saks Fifth Avenue
9. Sears
10.Wal-Mart
Interval
Scale
Preference Ratings
1-7
A Classification of Scaling Techniques
Comparative Scaling Techniques
Paired Comparison Scaling
A respondent is presented with two objects and asked to select one according to some criterion.
The data obtained are ordinal in nature.
Paired comparison scaling is the most widely-used comparative scaling technique.
With n brands, [n(n - 1) /2] paired comparisons are required.
Under the assumption of transitivity, it is possible to convert paired comparison data to a rank order.
Obtaining Shampoo Preferences
Using Paired Comparisons
Instructions: We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of shampoo you would prefer for personal use.
Recording Form:
aA 1 in a particular box means that the brand in that column was preferred over the brand in the corresponding row. A 0 means that the row brand was preferred over the column brand. bThe number of times a brand was preferred is obtained by summing the 1s in each column.
Paired Comparison Selling
The most common method of taste testing is paired comparison. The consumer is asked to sample two different products and select the one with the most appealing taste. The test is done in private and a minimum of 1,000 responses is considered an adequate sample. A blind taste test for a soft drink, where imagery, self-perception and brand reputation are very important factors in the consumer’s purchasing decision, may n ...
Value Based Pricing Strategy Powerpoint Presentation SlidesSlideTeam
"You can download this product from SlideTeam.net"
Introducing Value-Based Pricing Strategy PowerPoint Presentation Slides. With the help of a premium pricing PPT template, you can conduct market research in order to learn about customer demographics. By using this demand-based pricing PowerPoint presentation, you can highlight on various kinds of planning related to cost, competitor, and value-based factors. You can emphasize the steps which lead to the execution of data, thus you mention it using product preference analysis PPT visuals. By using a value-pricing plan PowerPoint presentation complete deck, you can create a report on the buyer’s personal data. With the help of pricing strategies PPT template, you can create a buyer’ persona on the basis of demographic, mindset, language, and customer journey. This premium pricing PowerPoint presentation contains sheets, charts, reports, and graphs with which you can create a market segment record. The product preference analysis PPT comprises content ready twenty-four slides to create a comprehensive presentation. Therefore, download this ready-to-use value pricing strategies PowerPoint presentation deck and improve profitability. https://bit.ly/34Pp7RN
Prediction of Crime Type plays a vital role in preventing crime in the society as well as assisting law agencies to design optimal strategies to ward off crime happenings in turn increasing public safety and decreasing economical loss.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
Predictive analytics of students' academic performance can help decision makers take appropriate actions at the right moment and plan appropriate training in order to improve the student’s success rate.
This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
This overview discusses the predictive analytical technique known as Gradient Boosting Regression, an analytical technique that explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable. Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. The Gradient Boosting Regression technique is useful in many applications, e.g., targeted sales strategies by using appropriate predictors to ensure accuracy of marketing campaigns and clarify relationships among factors such as seasonality, product pricing and product promotions, or for an agriculture business attempting to ascertain the effects of temperature, rainfall and humidity on crop production. Gradient Boosting Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
sing advanced analytics to identify quality issues will improve production processes, protect the business against liability claims and allow the organization to focus on quality issues and change product design and/or processes.
Predictive analytics for maintenance management can take the guesswork out of equipment maintenance, which parts to order and when equipment should be replaced.
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Predictive analytics for human resource attrition identifies areas of dissatisfaction, analyzes processes, benefits, training and environs to improve retention.
Predictive Analytics for customer targeting identifies buying frequency, what causes customers to buy, factors informing purchases and messaging by segment.
The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. It is useful for recognizing patterns and for estimating. The KNN Classification algorithm is useful in determining probable outcome and results, and in forecasting and predicting results, given the existence of multiple variables.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
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✅With one keyword, generate complete funnels, websites, landing pages, and more.
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
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✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
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✅Choose the temperature of the content and control its randomness.
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✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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5. Terminologies
Decision tree : It’s a powerful and popular tool for classification and prediction in form of a tree structure
Predictors and Target variable :
Target variable usually denoted by Y , is the variable being predicted and is also called dependent variable, output variable, response variable
or outcome variable (Ex : One highlighted in red box in image below)
Predictor, sometimes called an independent variable, is a variable that is being used to predict the target variable ( Ex : variables highlighted in
green below )
Here the predictors highlighted in green box above which consist of wine attributes are used to predict the target variable that is Quality of a
wine (labeled as Quality_category) highlighted in red box above
6. Leaf Node :
Terminal node in a decision
tree where there are no
further splits
Interior Node :
The non leaf
nodes. Also
called decision
nodes
Splitting :
It is a process of
dividing a node into
two or more sub-
nodes.
Root Node :
The top most node
in a tree
Terminologies
7. Terminologies
Each internal (non-leaf)
node denotes a test on
a feature/predictor
Each leaf represents a value
of the target variable /class
label given the values of the
input variables represented
by the path from the root to
the leaf
Each branch
represents the
outcome of a test
9. Types of Decision Tree
Examples
Based on the historical data related to credit card
payments , loan payments , delinquency rate ,
outstanding balance we want to classify/divide the
customers into defaulters and non defaulters.
To access the characteristics of a customer such as his
purchase frequency, income , age, type of bank account,
occupation etc. that leads to purchase/non purchase of a
particular banking product such as installment loan ,
personal loan, checking account etc. Here classification tree
will classify the customers into purchasers and non
purchasers
There are two basic types of decision tree :
• Classification
• Regression
Classification trees are needed when target
variable is categorical and as the name
implies are used to classify/divide the data
into these predefined categories of a target
variable.
10. Types of Decision Tree
•Based on customer’ past
behavioral data on a retail website
such as days from last purchase,
brand preference, income , age ,
gender, website visits , location,
total amount of purchase so far
etc., if we want to predict the
purchase amount by each
customer then regression trees
are useful (Here the target
variable would be purchase
amount)
Examples
:
Regression trees are needed when the target
variable is numeric
11. Types of Decision Tree
Similarly, regression tree can also be used to
identify the market segment who is more
likely to respond to a future mailing.
For instance the segments (green box in
image below) having response rate higher
than overall response rate can be targeted
first as they will require little effort to obtain.
Where as different marketing strategy needs
to be devised for lower segments (segments
having response rate less than overall - red
box in image below)
12. Classification Tree
Let’s say we have only two predictors : level
of Alcohol and free sulfur dioxide in a wine
and we want to predict if a wine quality
(target variable) will be High or Low
•Since the target variable wine quality here
contains categorical values (High & low) , the
classification method will be applicable here
as the predictors will be classifying the data
into high & low.
•Decision tree splits the nodes on all
available variables and then selects the split
which results in most homogeneous/pure
sub-nodes.
•For example, if the target can be either yes
or no (will or will not increase spending), the
objective is to produce nodes where most of
the cases will increase spending or most of
the cases will not increase spending.
13. How Does A Tree Decide Where To Split
This is where decision
tree helps, it will
segregate the students
based on all values of
three variables
and identify the variable,
which creates the best
homogeneous sets of
students.
Now, I want to create a
model to predict who will
play cricket during leisure
period?
15 out of these 30 play
cricket in leisure time.
Let’s say we have a
sample of 30 students
with three variables
Gender (Boy/ Girl), Class
(IX/ X) and Height (5 to 6
feet).
14. Homogeneous Nodes
In the snapshot below, you can see that variable Gender is able to identify best homogeneous sets compared to the
other two variables.
Here most of the cases (65%) play cricketHere most of the cases
(80%)
Don’t play cricket
Hence Homogeneous
15. How To Interpret The
Classification Tree Output
In our data if it turns out that most of the
wines containing alcohol level <11 turned out
to be of low quality (hence homogeneous)
then the first split happens based on Alcohol
level and it becomes a top node in the tree
Total number of
cases with
prediction = low for
wine quality
Total number of
cases with
prediction = high for
wine quality
16. How To Interpret The
Classification Tree Output
Further , if alcohol >=12 then it classifies the
wine to be of high quality else low quality (As
seen in red box in image below)
The cases/records falling in high quality are
further tested with free sulfur dioxide level. If
free sulfur dioxide is >=28 and alcohol is also
>=12 then such wines are classified to be of
High quality. (As seen in green box in image
below)
But wines with alcohol >=12 but not having
free sulfur dioxide >=28 are classified to be
of low quality. (As seen in blue box in image
below)
17. Method: Regression
Regression-type trees are generally
applicable where we attempt to predict the
values of a numeric target variable from one
or more numeric and/or categorical predictor
variables.
For example, we may want to predict the
quality of wine (a numeric target variable)
from various other predictors such as volatile
acidity in wine , alcohol level in wine etc.
In this case the leaf nodes will contain
predicted wine quality based on wine
attributes such as alcohol and volatility as
shown below.
18. Method : Regression
Let’s take an example of predicting the wine
quality on a scale of 3 to 10 based on
predictors such as alcohol level , free sulfur
dioxide level , volatility etc.
19. Method : Regression
As seen in the red box in image below , the
first split is again based on alcohol level as
we observed in the output of classification
tree example.
The similar type of pattern is shown up here
wherein the quality is predicted to be high in
case of free sulfur >=24 and alcohol >=12
(Blue box in image below)
Additional pattern observed here is that the
wine quality is also dependent on volatility
level. Quality is high in case of volatility level
<0.21 (Purple box below)
21. Max Depth
It sets the maximum depth of any node of
the final tree, with the root node counted as
depth 0.
Lesser this number, lesser the length of tree.
For instance, setting max depth=2 while
generating a classification tree to predict
wine quality will lead to output as shown
below:
Depth 1
Depth 2
Depth 0
22. Depth 1
Depth 2
Depth 3
Depth 0Max Depth
Similarly , setting max depth = 3
will give following output :
Hence, higher the max depth,
lengthier the final tree.
Lengthier trees are generally not
reliable as they tend to have
nodes with very less records so
the tree would have poor
generalizability
24. Input Wizard Sample For Selecting Target
Variables And Predictors
Predictors and target
variable should be
selected using input
wizard as shown
below
Select the variables you want
to use
for prediction of selected
target variable
Purchase frequency
Age
Gender
Income
Website visits
Select the variable
you would like to
predict (Target
variable):
Purchase frequency
Age
Gender
Income
Website visits
Purchase
1 2
25. 3 4
High
Age
Medium Low
<= 18 18 to 25 >=25
Male Female
Gender
Method
Impurity
Max
Depth
# Classes in
target variable
Classification
Gini
Two
Categorical
predictors
Purchase Frequency
Age
Gender
Select the classes to include
Purchase frequency
Tuning parameters
Note : Tuning parameters are explained in next section
Assuming the target
variable contains
yes/no values
Input Wizard Sample For Categorical
Predictors’ Class Selection & Tuning
Parameters
27. Please note : Spark expects to give input for these parameters instead of auto detection
Sample Output Formats
1.Method
When target variable type is
numeric , regression should
be auto selected and in
case of categorical target
variable type , classification
should be auto selected
Input control type : Static
label
2.Impurity
If method=classification
then impurity should be set
to gini automatically
If method=regression then
impurity should be set to
variance automatically
Input control type : Static
label
3.Categorical
predictors info
Categorical predictors and
their class values should be
auto detected
Input control type for
categorical predictors list :
Static label
Input control type for
classes selection : Multiple
checkbox buttons
4. Max depth :
Input control type :
Editable slider with numeric
value label
(Suggested value : 3 to 5)
5. Number of
classes (Only for
method =
classification) :
This value is based on total
number of classes present in
the target variable.
For example in wine quality
classification case , the total
classes of wine quality are
two : high & low
Input control type : Static
label
33. Limitations Of Decision Tree
Frequent changes to the data lead
to substantial differences in the
output , hence decision tree should
not be applied on data which is
fluctuating significantly.
There has to be predefined classes
for target variable (The categories
to which each record belongs for
classification tree) in the dataset.
Decision trees are prone to errors
in classification problems with
target variable containing many
classes and training dataset
containing relatively small number
of records.
• Hence total records in a training dataset
must be large in proportion to the total
classes of a target variable(There is no
thumb rule on how much larger the size of
records should be compared to target
variable classes)
34. Business Problem 1 – Classification tree
• Which customer segments should be targeted for increasing the subscription
rate of a term deposit product
• In this case, the classification tree can be used to access the characteristics of
customers that lead to subscription / non subscription of a term deposit
product targeted in direct marketing campaign
• Here the target variable would be the column of whether the customer that
was called during a direct marketing campaign , subscribed to a term deposit
product (“yes” if subscribed else “no”)
35. The Dataset
• Let’s say we have following customer attributes :
oAge
oJob type
oMarital status
oEducation
oAccount default status
oLoan status
oContact type
oOutcome of previous contact Target variable
Predictors
As shown above, we want to
classify customer attributes
such as
Age (numeric predictor), prior
loan status (categorical
predictor) ,
marital status (categorical
predictor) etc. into subscribers
and non subscribers of term
deposit product (target variable
classes)
36. Bar plot in leaf nodes show break up of yes and no classes
in the node with 0 to 1 scale in right side of the bar plot
indicating percentage of yes and no in that node and n
showing number of records belonging to that leaf node
Output Tree 1
37. Interpretation of tree output
As per the tree output, loan
status came out to be the best
predictor for term deposit
product purchase
Customers with prior loan and
marital status : “married”
outperforms all other segments
(Highlighted through blue
dashed line)
Also the customers with no prior
loans and age > 60 has the
second highest propensity to
purchase term deposit product
(Highlighted through green
dashed line)
Moreover , within the segment
with no prior loans , the singles
with age <=22 seem to be out
performing age >22 segment in
terms of term deposit product
purchase (Highlighted through
black dashed line)
38. How Splits And Terminal Nodes Are
Generated
Term deposit
purchased : Yes
Term deposit
purchased : No
Total records (%)
Prior Loan : Yes 10% 6% 16%
Prior Loan : No 14% 70% 84%
Marital Status No Yes Total records (%)
Divorced 13% 26% 39%
Married 21% 19% 40%
Single 6% 15% 21%
Decision tree chooses the predictor most
predictive of the target class
Here in our case , most of the records (84% of
records in dataset) contain prior loan status :
no and only 16% have loan status : Yes
Within loan status : no , 70% population don’t
purchase term deposit.
None of the other predictors have such homogeneity
with respect to term deposit purchase
For example marital status categories breakup is as
follows :
Thus due to relatively low homogeneity of other variables such as marital status, loan status was chosen as an attribute to create the first split
Similarly the sub nodes’ split happen using same homogeneity criteria
Terminal nodes are those nodes which can’t be split further due to the stopping criteria such as max depth (when maximum depth defined is 3, then node
splitting stops happening when tree depth =3 is achieved and last generated nodes become the terminal nodes )
39. Output 2 : Accuracy Of Prediction
No Yes
No 38,439 6,772
Yes 0 0
Actualclasses
Predicted classes
Accuracy = sum of boxes highlighted in red / all boxes = 38,439/(38,439+6,772+0+0) =
85.02%
Hence the sample decision tree model we just built is 85% accurate
and there is 15% chance of error here
Actual versus predicted table shows
how many classes are predicted
correctly by decision tree as shown
below :
40. Business Benefits
The segments
highlighted in black ,
blue and green in tree
output 1 are the low
hanging fruits requiring
less efforts to obtain so
no need to devise a
different target
marketing strategy for
these segments
The segments having
highest number of “No's”
(which are not
highlighted in tree
output 1) need to be
targeted in a different
and more efficient way to
convert them into
purchasers. For example
customers with marital
status : single/divorced.
Thus segmenting
customers based on their
propensity to buy/not
buy a product can aid in
devising better and
efficient target
marketing strategy in
order to convert more
non purchasers into
purchasers and in turn
increasing the product
penetration.
41. Use Case 2 – Classification Tree
Business benefit:
•Bank can decide on which customer
segments are eligible for any type of loan
versus which customer segments should
be denied any loan as they are likely to
default.
•This way risker customers are identified
easily and bank can avert the risk of
delinquencies
Business problem :
Based on the historical customer
attributes such as his/her credit card
payments ,loan payments ,outstanding
balance etc. a bank needs to classify the
customers into defaulters and non
defaulters
•In this case, the classification tree can be
used to access the characteristics of
customers that are likely to default
•Here the target variable would be a
column of whether customer has
defaulted previously or not (“yes” if
defaulted else “no”)
42. Use Case 3 – Regression Tree
Business benefit:
• Online retailers can identify the customer
segments which have higher capacity to
purchase and can design special marketing
strategy for such segments as these
segments are their main revenue drivers.
• This way premium customers can be given
special attention to retain their loyalty and in
turn revenue can be increased.
Business problem
Based on customers’ attributes and past
online shopping behavioral data, an online
retail giant such as Amazon/Flipkart wants to
predict the future purchase amount of
customers
• Here predictors can be customer's ‘days from
last purchase’, ‘brand preference’, ‘income’ ,
‘age’ , ‘gender’, ‘website visits’ , ‘location’,
‘total amount of purchase so far’ etc.,
• As the target variable is numeric (purchase
amount) , regression tree can be used to
predict the purchase amount by different
type of customer segments.
43. Use case 4 – Regression Tree
Business benefit:
• As soon as the new order arrives , a
service provider can provide
estimated completion time to a
customer based on the general
pattern observed through
regression tree model
• Proper workforce allocation and
planning
• Avoiding revenue leakage through
prevention of delay fine
Business problem :
• Predicting order completion time
for telecom service provider
• Predictors in this case can be : user
location , work force availability,
distance from nearest network
junction, average time taken in last
6 months , average historical delay
in last 6 months etc.
• Target variable here would be turn
around time of order completion
44. Want to Learn
More?
Get in touch with us @
support@Smarten.com
And Do Checkout the Learning section
on
Smarten.com
June 2018