DECISION TREE CONCEPT by Saikat Garai
•
0 likes•19 views
This document discusses different types of decisions, decision trees, and how decision trees are used for classification. It describes strategic, administrative, and operating decisions. It defines a decision tree as a graphical representation of possible solutions to a decision based on conditions, and discusses decision nodes, chance nodes, and end nodes. It explains how decision trees are used for classification, with training and test datasets containing attributes like weather conditions to predict if people will play tennis or not. Finally, it provides an example of rules generated from a decision tree to classify days based on weather.
Report
Share
Report
Share
Recommended
Decision tree
The document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the different types of nodes in a decision tree including decision, chance, and end nodes. An example decision tree is provided about weekend plans depending on whether parents visit and the weather. The document outlines how to draw a decision tree and its advantages such as being simple to understand and having value even with little data.
decision tree in statistics last chapter
This document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the key elements of a decision tree including decision nodes, chance nodes, and end nodes. An example decision tree is provided showing a scenario of weekend plans depending on parents' visit and weather conditions. Advantages of decision trees are that they are simple to understand, can provide insights even with little data, use a transparent model, and can be combined with other techniques.
decisiontree-161204090249.pptx
This document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the key elements of a decision tree including decision nodes, chance nodes, and end nodes. An example decision tree is provided showing a scenario of weekend plans depending on parents' visit and weather conditions. Advantages of decision trees are that they are simple to understand, can provide insights even with little data, use a transparent model, and can be combined with other techniques.
Decision theory & decisiontrees
A decision tree is a diagram that visually represents decisions, uncertainties, and outcomes of a complex decision-making process. It breaks down a complex problem into a step-by-step process. The document provides examples of how to construct a decision tree by defining decision points and possible outcomes as branches. It also explains how to evaluate a decision tree by assigning values and probabilities to outcomes and working backwards to calculate expected values at decision points in order to determine the optimal decision.
Lect9 Decision tree
Decision trees classify instances by starting at the root node and moving through the tree recursively according to attribute tests at each node, until a leaf node determining the class label is reached. They work by splitting the training data into purer partitions based on the values of predictor attributes, using an attribute selection measure like information gain to choose the splitting attributes. The resulting tree can be pruned to avoid overfitting and reduce error on new data.
Decision tree
The document defines and provides examples of decision trees. It explains that decision trees use a branching graph structure to illustrate all possible outcomes of a decision. They are used in operations research and decision analysis to help identify the most likely strategy to reach a goal. The document provides an example of an email management decision tree and explains how decision tree analysis involves forecasting outcomes and assigning probabilities. It outlines some common applications of decision trees and provides an example decision tree for weekend activity choices based on weather, finances, and parental visits.
Acid Rain - Formative Assessment
The document provides guidance for students on an assessment for a geography standard involving analysis of a contemporary issue. It outlines the criteria for different achievement levels and provides examples and tasks to help students understand the requirements. Key points covered include identifying the differences between not achieved, achieved, merit and excellence; describing the geographic nature and viewpoints around an issue; evaluating courses of action and making justified recommendations.
Recommended
Decision tree
The document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the different types of nodes in a decision tree including decision, chance, and end nodes. An example decision tree is provided about weekend plans depending on whether parents visit and the weather. The document outlines how to draw a decision tree and its advantages such as being simple to understand and having value even with little data.
decision tree in statistics last chapter
This document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the key elements of a decision tree including decision nodes, chance nodes, and end nodes. An example decision tree is provided showing a scenario of weekend plans depending on parents' visit and weather conditions. Advantages of decision trees are that they are simple to understand, can provide insights even with little data, use a transparent model, and can be combined with other techniques.
decisiontree-161204090249.pptx
This document discusses decision making trees. It defines a decision tree as a graphical representation of possible solutions to a decision based on certain conditions. It describes the key elements of a decision tree including decision nodes, chance nodes, and end nodes. An example decision tree is provided showing a scenario of weekend plans depending on parents' visit and weather conditions. Advantages of decision trees are that they are simple to understand, can provide insights even with little data, use a transparent model, and can be combined with other techniques.
Decision theory & decisiontrees
A decision tree is a diagram that visually represents decisions, uncertainties, and outcomes of a complex decision-making process. It breaks down a complex problem into a step-by-step process. The document provides examples of how to construct a decision tree by defining decision points and possible outcomes as branches. It also explains how to evaluate a decision tree by assigning values and probabilities to outcomes and working backwards to calculate expected values at decision points in order to determine the optimal decision.
Lect9 Decision tree
Decision trees classify instances by starting at the root node and moving through the tree recursively according to attribute tests at each node, until a leaf node determining the class label is reached. They work by splitting the training data into purer partitions based on the values of predictor attributes, using an attribute selection measure like information gain to choose the splitting attributes. The resulting tree can be pruned to avoid overfitting and reduce error on new data.
Decision tree
The document defines and provides examples of decision trees. It explains that decision trees use a branching graph structure to illustrate all possible outcomes of a decision. They are used in operations research and decision analysis to help identify the most likely strategy to reach a goal. The document provides an example of an email management decision tree and explains how decision tree analysis involves forecasting outcomes and assigning probabilities. It outlines some common applications of decision trees and provides an example decision tree for weekend activity choices based on weather, finances, and parental visits.
Acid Rain - Formative Assessment
The document provides guidance for students on an assessment for a geography standard involving analysis of a contemporary issue. It outlines the criteria for different achievement levels and provides examples and tasks to help students understand the requirements. Key points covered include identifying the differences between not achieved, achieved, merit and excellence; describing the geographic nature and viewpoints around an issue; evaluating courses of action and making justified recommendations.
Decision trees & random forests
The document discusses decision trees and random forests machine learning algorithms. It explains that decision trees are simple algorithms that can be used for classification and regression problems. Random forests improve on decision trees by constructing multiple trees on randomly sampled data and averaging their predictions, which helps prevent overfitting issues. The document provides examples of how decision trees are constructed and how random forests make predictions through voting or averaging tree predictions.
Introduction to Boosted Trees by Tianqi Chen
This document provides an introduction to boosted trees. It reviews key concepts in supervised learning such as loss functions, regularization, and the bias-variance tradeoff. Regression trees are described as a model that partitions data and assigns a prediction score to each partition. Gradient boosting is presented as a method for learning an ensemble of regression trees additively to minimize a given loss function. The learning process is formulated as optimizing an objective function that balances training loss and model complexity.
Assignment 6.1
This document discusses various tools and frameworks for ethical decision making, including decision analysis, IRAC analysis, the Five Whys technique, the DMAIC framework, the Seven Quality Tools, rational choice theory, image theory, and decision mapping. It provides an overview and brief description of each tool or framework and how they can be applied to evaluate situations and make choices consistent with ethical principles while considering consequences and values. The goal is to understand different approaches for structuring complex decisions and determining the best choice.
computational_thinking_gcse.pptx
Computational thinking allows us to break down complex problems into smaller, more manageable parts using techniques like decomposition, pattern recognition, abstraction, and algorithms. The document discusses these four techniques of computational thinking and provides examples and tasks to illustrate how to apply them to solve problems. Specifically, it breaks down problems like meeting with friends, completing a video game level, and baking a cake to identify patterns and irrelevant details in order to develop step-by-step algorithms to solve the overall problems.
2167 class 2 fin statements & ethics
The document summarizes the agenda and key topics for a business decision making course. It includes discussions on financial statements, group exercises, case preparation, forecasting, and survival strategies. Survival items are listed and students are asked to prioritize them in order of importance for survival in harsh winter conditions in Northern Canada.
Seminar 7 and 8 - Complete The Flower Exercise and Choose Your Own Creative A...
This is the last set of seminars before individual portfolio submission in the first half of the module 'Employability, Creativity, and Personal Development'.
We do two exercises in this seminar.
For Seminar 7, you will complete the Flower Exercise from the book 'What Color is Your Parachute' which will help you craft and design your ideal career.
For Seminar 8, you choose one activity that you wish to do. There are 6 activities to choose from:
1) Create your own personal logo.
2) Coach yourself for the upcoming 12 months.
3) Choose your role models and learn from them.
4) Create a system for tracking your job applications, firms, and positions.
5) Create a SWOT analysis of yourself in the job market and reflect on your employability.
6) Create your own Netflix show - imagine wildly.
You can try any of these activities as they will help you tremendously in designing your life and your career after graduation.
These activities will help you create a better self personally and professionally.
Enjoy!
Module 09 demos and retrospectives gla
This document provides an overview of demonstrations, retrospectives, and the Scrum process. It describes how daily standup meetings, iteration planning meetings, demonstrations of working software, and retrospective meetings fit into a sample two-week Scrum iteration cycle. Key aspects covered include defining "Done", planning and running iteration demos, and using Norm Kerth's Prime Directive and affinity mapping during retrospectives to identify one thing to improve for the next iteration.
Boosted tree
This document provides an introduction to boosted trees. It reviews key concepts in supervised learning like loss functions and regularization. Regression trees make predictions by assigning scores to leaf nodes. Gradient boosting is an algorithm that learns an ensemble of regression trees additively to minimize a loss function. It works by greedily adding trees to improve the model's fit on the training data based on the gradient of the loss function. The trees are learned one at a time by calculating the optimal split at each node to reduce a "structure score" measuring how well the split partitions the data.
Choices
The document describes an activity where participants are given sets of choices without context and asked to select one answer from each set. The purpose is to illustrate how unclear or misaligned goals and visions within a team can lead to frustration and incorrect decisions. Participants will feel confused making their selections. After completing the activity, the facilitator reveals the goal was to select words featured on the US flag, showing how shared context and understanding of goals can make decisions easier. The activity is meant to spark discussion around ensuring all team members understand and share the same goals and visions to eliminate frustration and make better decisions.
13 Machine Learning Supervised Decision Trees
This document provides an overview of machine learning decision trees. It discusses how decision trees work by applying a sequence of simple decision rules to divide data into progressively smaller and more homogeneous groups. Decision trees can be used for classification or regression problems. The document focuses on ordinary binary classification trees, which use binary questions of the form "is attribute x less than or equal to a?" to split data. It explains key decision tree concepts like nodes, branches, and leaves and discusses algorithms for training decision trees by selecting the optimal attribute to test at each node based on criteria like probabilistic impurity.
Operational Research
This document provides information about decision trees, including:
1. Decision trees are graphical representations that present conditions and actions sequentially from starting point to various possible outcomes.
2. The key elements of decision trees are decision nodes, chance nodes, and end nodes. Decision nodes represent decision points, chance nodes represent uncertain events, and end nodes represent conclusions.
3. An example decision tree is presented to illustrate weekend activity choices based on weather, financial status, and whether parents are visiting.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...
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
Swarm n roll
This document provides instructions and materials for a board game called "Swarm'n'roll" that is intended to teach agile principles like swarming, work in progress limits, and cycle time. It can be played by 3-6 players who take on roles in a software development team. The game is played over two iterations, with players rolling dice to simulate progress and coordinating work by moving tasks and items between workflow states on a board. Scorecards are used to track metrics like items completed and cycle times for discussion at a post-game debriefing.
Lecture 14 project planning
The document discusses various tools and techniques for project planning and problem solving, including:
1) Problem solving steps such as identifying reasons for improvement, analyzing the current situation, developing countermeasures, and standardizing solutions.
2) Graphical tools like storyboards, fishbone diagrams, histograms, pie charts, and SWOT charts that can help structure and communicate ideas.
3) Project management methods like Gantt charts, PERT networks, and critical path methods for planning, scheduling, and tracking progress of a project.
Decision Tree Machine Learning Detailed Explanation.
Decision Tree is a machine learning algorithm that can be used for both classification and regression problems. It creates a flow-chart like structure starting with an initial node which branches out further into other sub-nodes. The documents discuss decision tree structure, splitting criteria, feature selection and real world applications. Code in Python is provided to demonstrate building a basic decision tree classifier on the iris dataset.
Decision trees
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Biotechnology Ethics Slideshow Formatting and Grading Criteria
This document provides instructions for students to create an ethics slideshow presentation on a biotechnology issue. Students will be graded on two aspects: how well they follow the strategy for values clarification outlined in the text, and the formatting of their slideshow. The strategy involves identifying the problem, listing possible solutions, examining the pros and cons of each, ranking the solutions, and deciding whether to take a position. Students are instructed to research the issue, gathering facts and opinions from credible sources. Their slideshow should apply the strategy, cite sources, use proper grammar and formatting, and be easy to read.
Decision making Planning Organisation 1216640408319045 8
This document provides guidance on various decision-making tools and planning and organization strategies. It discusses tools like PMI (Plus, Minus, Interesting), Six Colour Thinking, and Egan's Skilled Helper Model to aid decision-making. For planning and organization, it recommends prioritizing tasks by importance and urgency, breaking large tasks into smaller steps, and using a diary or calendar to track tasks. Group decision-making tips include establishing ground rules, aiming for consensus, and considering decisions carefully rather than rushing.
SOCIO 120 Annotated Bibliography that provides a.docx
SOCIO 120
Annotated Bibliography that provides an overview of research and information relative to at least
three sources of information they used when collaborating information.
1. Research and generate two paragraphs with the information related to the chosen topic with
answered to the provided questions.
2. Must use THREE sources/references.
3. Summarize, analyze, and synthesis the THREE resources/references in APA format. (How
did these sources/references help you develop this paper)
4. Provide important facts and information one needs to know about this particular issue (this
should be informed by research from journal articles, newspaper articles, etc.) that are
relevant in today’s society.
5. Which sociological theoretical perspective(s) do you think best explain(s) this issue?
6. What is being done to address this issue (public policy, legislation, programs, social
movements, non-profit agencies, etc.)?
7. What should be done to address this issue?
MUST BE:
TIMES NEW ROMAN TEXT
12 FONT
MUST BE IN APA FORMAT.
LIST OF REFERENCES
Topic : DACA and DREAM Act.
What is DACA? (Deferred Action for Childhood Arrivals)
How did DACA come into action?
How does DACA reflect from the southern border of the United States?
How does it benefit children and adults that were brought into the U.S as children?
How much is the cost to keep all paperwork up to date?
What is the DREAM Act?
How does/did this act help immigrants?
How did it come into action?
How does this benefit people that are not U.S citizens?
Web-Centric Alternative Certification Program 2005
Lesson Planning Worksheet (Please save this form on your computer to complete, and upload your completed one day lesson plan.)
TEKS (What TEKS will this lesson cover?)
Texas Essential Knowledge & Skills
Content-specific & technology
Objectives (What will my students learn, comprehend, and master?)
Begins with action verbs or might be written in easier to understand language for children to understand
Written before lesson plan - the specific goals of what is to be taught and accomplished
Precisely states content-specific target
What students have to achieve to learn, comprehend, and master
Tells students what must be accomplished
Helps teachers view if it was accomplished
Assessment (How will I assess their progress?)
Beginning with the end in mind
Must be based on same behavior stated in objective
Tells how well students actually attained the objectives.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
More Related Content
Similar to DECISION TREE CONCEPT by Saikat Garai
Decision trees & random forests
The document discusses decision trees and random forests machine learning algorithms. It explains that decision trees are simple algorithms that can be used for classification and regression problems. Random forests improve on decision trees by constructing multiple trees on randomly sampled data and averaging their predictions, which helps prevent overfitting issues. The document provides examples of how decision trees are constructed and how random forests make predictions through voting or averaging tree predictions.
Introduction to Boosted Trees by Tianqi Chen
This document provides an introduction to boosted trees. It reviews key concepts in supervised learning such as loss functions, regularization, and the bias-variance tradeoff. Regression trees are described as a model that partitions data and assigns a prediction score to each partition. Gradient boosting is presented as a method for learning an ensemble of regression trees additively to minimize a given loss function. The learning process is formulated as optimizing an objective function that balances training loss and model complexity.
Assignment 6.1
This document discusses various tools and frameworks for ethical decision making, including decision analysis, IRAC analysis, the Five Whys technique, the DMAIC framework, the Seven Quality Tools, rational choice theory, image theory, and decision mapping. It provides an overview and brief description of each tool or framework and how they can be applied to evaluate situations and make choices consistent with ethical principles while considering consequences and values. The goal is to understand different approaches for structuring complex decisions and determining the best choice.
computational_thinking_gcse.pptx
Computational thinking allows us to break down complex problems into smaller, more manageable parts using techniques like decomposition, pattern recognition, abstraction, and algorithms. The document discusses these four techniques of computational thinking and provides examples and tasks to illustrate how to apply them to solve problems. Specifically, it breaks down problems like meeting with friends, completing a video game level, and baking a cake to identify patterns and irrelevant details in order to develop step-by-step algorithms to solve the overall problems.
2167 class 2 fin statements & ethics
The document summarizes the agenda and key topics for a business decision making course. It includes discussions on financial statements, group exercises, case preparation, forecasting, and survival strategies. Survival items are listed and students are asked to prioritize them in order of importance for survival in harsh winter conditions in Northern Canada.
Seminar 7 and 8 - Complete The Flower Exercise and Choose Your Own Creative A...
This is the last set of seminars before individual portfolio submission in the first half of the module 'Employability, Creativity, and Personal Development'.
We do two exercises in this seminar.
For Seminar 7, you will complete the Flower Exercise from the book 'What Color is Your Parachute' which will help you craft and design your ideal career.
For Seminar 8, you choose one activity that you wish to do. There are 6 activities to choose from:
1) Create your own personal logo.
2) Coach yourself for the upcoming 12 months.
3) Choose your role models and learn from them.
4) Create a system for tracking your job applications, firms, and positions.
5) Create a SWOT analysis of yourself in the job market and reflect on your employability.
6) Create your own Netflix show - imagine wildly.
You can try any of these activities as they will help you tremendously in designing your life and your career after graduation.
These activities will help you create a better self personally and professionally.
Enjoy!
Module 09 demos and retrospectives gla
This document provides an overview of demonstrations, retrospectives, and the Scrum process. It describes how daily standup meetings, iteration planning meetings, demonstrations of working software, and retrospective meetings fit into a sample two-week Scrum iteration cycle. Key aspects covered include defining "Done", planning and running iteration demos, and using Norm Kerth's Prime Directive and affinity mapping during retrospectives to identify one thing to improve for the next iteration.
Boosted tree
This document provides an introduction to boosted trees. It reviews key concepts in supervised learning like loss functions and regularization. Regression trees make predictions by assigning scores to leaf nodes. Gradient boosting is an algorithm that learns an ensemble of regression trees additively to minimize a loss function. It works by greedily adding trees to improve the model's fit on the training data based on the gradient of the loss function. The trees are learned one at a time by calculating the optimal split at each node to reduce a "structure score" measuring how well the split partitions the data.
Choices
The document describes an activity where participants are given sets of choices without context and asked to select one answer from each set. The purpose is to illustrate how unclear or misaligned goals and visions within a team can lead to frustration and incorrect decisions. Participants will feel confused making their selections. After completing the activity, the facilitator reveals the goal was to select words featured on the US flag, showing how shared context and understanding of goals can make decisions easier. The activity is meant to spark discussion around ensuring all team members understand and share the same goals and visions to eliminate frustration and make better decisions.
13 Machine Learning Supervised Decision Trees
This document provides an overview of machine learning decision trees. It discusses how decision trees work by applying a sequence of simple decision rules to divide data into progressively smaller and more homogeneous groups. Decision trees can be used for classification or regression problems. The document focuses on ordinary binary classification trees, which use binary questions of the form "is attribute x less than or equal to a?" to split data. It explains key decision tree concepts like nodes, branches, and leaves and discusses algorithms for training decision trees by selecting the optimal attribute to test at each node based on criteria like probabilistic impurity.
Operational Research
This document provides information about decision trees, including:
1. Decision trees are graphical representations that present conditions and actions sequentially from starting point to various possible outcomes.
2. The key elements of decision trees are decision nodes, chance nodes, and end nodes. Decision nodes represent decision points, chance nodes represent uncertain events, and end nodes represent conclusions.
3. An example decision tree is presented to illustrate weekend activity choices based on weather, financial status, and whether parents are visiting.
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...
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
Swarm n roll
This document provides instructions and materials for a board game called "Swarm'n'roll" that is intended to teach agile principles like swarming, work in progress limits, and cycle time. It can be played by 3-6 players who take on roles in a software development team. The game is played over two iterations, with players rolling dice to simulate progress and coordinating work by moving tasks and items between workflow states on a board. Scorecards are used to track metrics like items completed and cycle times for discussion at a post-game debriefing.
Lecture 14 project planning
The document discusses various tools and techniques for project planning and problem solving, including:
1) Problem solving steps such as identifying reasons for improvement, analyzing the current situation, developing countermeasures, and standardizing solutions.
2) Graphical tools like storyboards, fishbone diagrams, histograms, pie charts, and SWOT charts that can help structure and communicate ideas.
3) Project management methods like Gantt charts, PERT networks, and critical path methods for planning, scheduling, and tracking progress of a project.
Decision Tree Machine Learning Detailed Explanation.
Decision Tree is a machine learning algorithm that can be used for both classification and regression problems. It creates a flow-chart like structure starting with an initial node which branches out further into other sub-nodes. The documents discuss decision tree structure, splitting criteria, feature selection and real world applications. Code in Python is provided to demonstrate building a basic decision tree classifier on the iris dataset.
Decision trees
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Biotechnology Ethics Slideshow Formatting and Grading Criteria
This document provides instructions for students to create an ethics slideshow presentation on a biotechnology issue. Students will be graded on two aspects: how well they follow the strategy for values clarification outlined in the text, and the formatting of their slideshow. The strategy involves identifying the problem, listing possible solutions, examining the pros and cons of each, ranking the solutions, and deciding whether to take a position. Students are instructed to research the issue, gathering facts and opinions from credible sources. Their slideshow should apply the strategy, cite sources, use proper grammar and formatting, and be easy to read.
Decision making Planning Organisation 1216640408319045 8
This document provides guidance on various decision-making tools and planning and organization strategies. It discusses tools like PMI (Plus, Minus, Interesting), Six Colour Thinking, and Egan's Skilled Helper Model to aid decision-making. For planning and organization, it recommends prioritizing tasks by importance and urgency, breaking large tasks into smaller steps, and using a diary or calendar to track tasks. Group decision-making tips include establishing ground rules, aiming for consensus, and considering decisions carefully rather than rushing.
SOCIO 120 Annotated Bibliography that provides a.docx
SOCIO 120
Annotated Bibliography that provides an overview of research and information relative to at least
three sources of information they used when collaborating information.
1. Research and generate two paragraphs with the information related to the chosen topic with
answered to the provided questions.
2. Must use THREE sources/references.
3. Summarize, analyze, and synthesis the THREE resources/references in APA format. (How
did these sources/references help you develop this paper)
4. Provide important facts and information one needs to know about this particular issue (this
should be informed by research from journal articles, newspaper articles, etc.) that are
relevant in today’s society.
5. Which sociological theoretical perspective(s) do you think best explain(s) this issue?
6. What is being done to address this issue (public policy, legislation, programs, social
movements, non-profit agencies, etc.)?
7. What should be done to address this issue?
MUST BE:
TIMES NEW ROMAN TEXT
12 FONT
MUST BE IN APA FORMAT.
LIST OF REFERENCES
Topic : DACA and DREAM Act.
What is DACA? (Deferred Action for Childhood Arrivals)
How did DACA come into action?
How does DACA reflect from the southern border of the United States?
How does it benefit children and adults that were brought into the U.S as children?
How much is the cost to keep all paperwork up to date?
What is the DREAM Act?
How does/did this act help immigrants?
How did it come into action?
How does this benefit people that are not U.S citizens?
Web-Centric Alternative Certification Program 2005
Lesson Planning Worksheet (Please save this form on your computer to complete, and upload your completed one day lesson plan.)
TEKS (What TEKS will this lesson cover?)
Texas Essential Knowledge & Skills
Content-specific & technology
Objectives (What will my students learn, comprehend, and master?)
Begins with action verbs or might be written in easier to understand language for children to understand
Written before lesson plan - the specific goals of what is to be taught and accomplished
Precisely states content-specific target
What students have to achieve to learn, comprehend, and master
Tells students what must be accomplished
Helps teachers view if it was accomplished
Assessment (How will I assess their progress?)
Beginning with the end in mind
Must be based on same behavior stated in objective
Tells how well students actually attained the objectives.
Similar to DECISION TREE CONCEPT by Saikat Garai (20)
Seminar 7 and 8 - Complete The Flower Exercise and Choose Your Own Creative A...
Seminar 7 and 8 - Complete The Flower Exercise and Choose Your Own Creative A...
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...
Decision Tree In R | Decision Tree Algorithm | Data Science Tutorial | Machin...
Decision Tree Machine Learning Detailed Explanation.
Decision Tree Machine Learning Detailed Explanation.
Biotechnology Ethics Slideshow Formatting and Grading Criteria
Biotechnology Ethics Slideshow Formatting and Grading Criteria
Decision making Planning Organisation 1216640408319045 8
Decision making Planning Organisation 1216640408319045 8
SOCIO 120 Annotated Bibliography that provides a.docx
SOCIO 120 Annotated Bibliography that provides a.docx
Microsoft PowerPoint - Lec 04 - Decision Tree Learning.pdf
Microsoft PowerPoint - Lec 04 - Decision Tree Learning.pdf
Recently uploaded
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Recently uploaded (20)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
DECISION TREE CONCEPT by Saikat Garai
- 1. Presented By:- Saikat Garai PRESIDENCY COLLEGE,BANGLORE
- 2. Types of Decision Strategic Decision: Concerned with structuring and acquisitor of the organization. Administrative Decision: Concerned with structure and acquisitor of the organization's resources so as to optimize the performance of the organization. Operating Decision: Concerned with day to day operations of the organization such as pricing, production scheduling, inventory levels etc.
- 4. Decision Tree Definition: A Decision tree is a graphical representation of possible solution to a decision based on certain conditions. It’s called a decision tree because it starts with a single box(or root), which then branches off into a number of solutions, just like a tree. OR it is the process of chosing a course of action from among alternatives to achieve a desired goal.
- 5. Nodes for making Decision tree Decision Nodes: Commonly Represented by squares. Change nodes: Represented by circle. End Nodes: Represented by triangles.
- 6. DECISION TREE • Classification scheme • Generates a tree and a set of rules • Set of record divide into two subsets Training set Test set • Attributes are divide into 2 types Numerical attribute Categorical attribute
- 8. DECISION TREE Decision tree to represent learned target functions Each internal node tests an attribute Each branch corresponds to attribute value Each leaf node assigns a classification Rules are easier for humans to understand
- 9. Output: A Decision Tree
- 10. Example So, let me explain this to you with an example. So, this is what I was mentioning that this table that we see represents the training set of the training examples. Let us see what this table means. In this in this toy example the instances are objects that you are talking about and nothing, but it a day, a day of the week some day of some season. So, each row in this table describes a day. So, there are there are D 1 to D 14, there are 14 days which are previous examples. And each day belongs to one of the two categories one of the two categories. If you look at the table in the slide, there are two categories whether people prefer to play tennis and outdoor sports on that day or does not prefer to play tennis on that day. So, each day belongs to two categories whether people play tennis or do not.
- 11. Rule 1: If it is sunny and humidity is high then don’t play Rule 2: If it is sunny and humidity is normal then play Rule 3: If it is overcast then play Rule 4: If it is rainy and windy then don’t play Rule 5: if it is rainy and not windy then play
- 12. Tree Construction Principle Generally building a tree involves two steps: Tree construction- recursively split the tree according to selected attributes Tree pruning- identify and remove the irrelevance braches
- 13. Thank you