The document discusses decision tree induction, which is a popular tool for classification and prediction. It describes how decision trees work by having internal decision nodes that split the data into branches, which end at leaf nodes that provide a class label or prediction. It also covers different algorithms for building decision trees like ID3, C4.5, and CART. The key steps in decision tree induction include selecting attributes to split on using metrics like information gain or Gini index, and pruning the fully grown tree to avoid overfitting.
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.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
Decision trees are a machine learning technique that use a tree-like model to predict outcomes. They break down a dataset into smaller subsets based on attribute values. Decision trees evaluate attributes like outlook, temperature, humidity, and wind to determine the best predictor. The algorithm calculates information gain to determine which attribute best splits the data into the most homogeneous subsets. It selects the attribute with the highest information gain to place at the root node and then recursively builds the tree by splitting on subsequent attributes.
Decision trees are a type of supervised machine learning that use a tree-like model to predict target variables. They work by splitting data into smaller and smaller groups (branches) based on attribute values, continuing until the groups only contain similar target variable values or cannot be split further. The tree consists of decision nodes that test attributes, branches representing the outcome of the tests, and leaf nodes that represent classifications or predicted target values. The ID3 algorithm builds decision trees by selecting the attribute that creates the most information gain at each split in a greedy, top-down manner.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
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.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like entropy, information gain, and how decision trees are constructed and evaluated. Examples are given to illustrate these concepts. The document concludes with strengths and weaknesses of decision tree algorithms.
Decision trees are a machine learning technique that use a tree-like model to predict outcomes. They break down a dataset into smaller subsets based on attribute values. Decision trees evaluate attributes like outlook, temperature, humidity, and wind to determine the best predictor. The algorithm calculates information gain to determine which attribute best splits the data into the most homogeneous subsets. It selects the attribute with the highest information gain to place at the root node and then recursively builds the tree by splitting on subsequent attributes.
Decision trees are a type of supervised machine learning that use a tree-like model to predict target variables. They work by splitting data into smaller and smaller groups (branches) based on attribute values, continuing until the groups only contain similar target variable values or cannot be split further. The tree consists of decision nodes that test attributes, branches representing the outcome of the tests, and leaf nodes that represent classifications or predicted target values. The ID3 algorithm builds decision trees by selecting the attribute that creates the most information gain at each split in a greedy, top-down manner.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
The document discusses decision tree algorithms. It begins with an introduction and example, then covers the principles of entropy and information gain used to build decision trees. It provides explanations of key concepts like evaluating decision trees using training and testing accuracy. The document concludes with strengths and weaknesses of decision tree algorithms.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
The document discusses decision tree induction algorithms. It begins with an introduction to decision trees, describing their structure and how they are used for classification. It then covers the basic algorithm for constructing decision trees, including the ID3, C4.5, and CART algorithms. Next, it discusses different attribute selection measures that can be used to determine the best attribute to split on at each node, including information gain, gain ratio, and the Gini index. It provides details on how information gain is calculated.
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
This document discusses data generalization and summarization techniques. It describes how attribute-oriented induction generalizes data from low to high conceptual levels by examining attribute values. The number of distinct values for each attribute is considered, and attributes may be removed, generalized up concept hierarchies, or retained in the generalized relation. An algorithm for attribute-oriented induction takes a relational database and data mining query as input and outputs a generalized relation. Generalized data can be presented as crosstabs, bar charts, or pie charts.
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
This document discusses decision trees, a classification technique in data mining. It defines classification as assigning class labels to unlabeled data based on a training set. Decision trees generate a tree structure to classify data, with internal nodes representing attributes, branches representing attribute values, and leaf nodes holding class labels. An algorithm is used to recursively split the data set into purer subsets based on attribute tests until each subset belongs to a single class. The tree can then classify new examples by traversing it from root to leaf.
This document provides an introduction to k-means clustering, including:
1. K-means clustering aims to partition n observations into k clusters by minimizing the within-cluster sum of squares, where each observation belongs to the cluster with the nearest mean.
2. The k-means algorithm initializes cluster centroids and assigns observations to the nearest centroid, recomputing centroids until convergence.
3. K-means clustering is commonly used for applications like machine learning, data mining, and image segmentation due to its efficiency, though it is sensitive to initialization and assumes spherical clusters.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
The document discusses the different phases of a compiler: lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It explains that a compiler takes source code as input and translates it into an equivalent language. The compiler performs analysis and synthesis in multiple phases, with each phase transforming the representation of the source code. Key activities include generating tokens, building a syntax tree, type checking, generating optimized intermediate code, and finally producing target machine code. Symbol tables are also used to store identifier information as the compiler runs.
Data Mining - Classification Of Breast Cancer Dataset using Decision Tree Ind...Sunil Nair
The document summarizes research on classifying breast cancer datasets using decision trees. The researchers used a Wisconsin breast cancer dataset containing 699 instances with 10 attributes plus a class attribute. They preprocessed the data to handle missing values, compared various classification methods, and achieved the best accuracy of 97% using decision trees with attribute selection. Issues addressed included unbalanced classes and future work proposed methods like clustering and multiple classifiers to further improve accuracy.
Random forests are an ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It improves upon decision trees by reducing variance. The algorithm works by:
1) Randomly sampling cases and variables to grow each tree.
2) Splitting nodes using the gini index or information gain on the randomly selected variables.
3) Growing each tree fully without pruning.
4) Aggregating the predictions of all trees using a majority vote. This reduces variance compared to a single decision tree.
The document discusses decision tree induction algorithms. It begins with an introduction to decision trees, describing their structure and how they are used for classification. It then covers the basic algorithm for constructing decision trees, including the ID3, C4.5, and CART algorithms. Next, it discusses different attribute selection measures that can be used to determine the best attribute to split on at each node, including information gain, gain ratio, and the Gini index. It provides details on how information gain is calculated.
This presentation introduces clustering analysis and the k-means clustering technique. It defines clustering as an unsupervised method to segment data into groups with similar traits. The presentation outlines different clustering types (hard vs soft), techniques (partitioning, hierarchical, etc.), and describes the k-means algorithm in detail through multiple steps. It discusses requirements for clustering, provides examples of applications, and reviews advantages and disadvantages of k-means clustering.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
This document discusses data generalization and summarization techniques. It describes how attribute-oriented induction generalizes data from low to high conceptual levels by examining attribute values. The number of distinct values for each attribute is considered, and attributes may be removed, generalized up concept hierarchies, or retained in the generalized relation. An algorithm for attribute-oriented induction takes a relational database and data mining query as input and outputs a generalized relation. Generalized data can be presented as crosstabs, bar charts, or pie charts.
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
This document discusses decision trees, a classification technique in data mining. It defines classification as assigning class labels to unlabeled data based on a training set. Decision trees generate a tree structure to classify data, with internal nodes representing attributes, branches representing attribute values, and leaf nodes holding class labels. An algorithm is used to recursively split the data set into purer subsets based on attribute tests until each subset belongs to a single class. The tree can then classify new examples by traversing it from root to leaf.
This document provides an introduction to k-means clustering, including:
1. K-means clustering aims to partition n observations into k clusters by minimizing the within-cluster sum of squares, where each observation belongs to the cluster with the nearest mean.
2. The k-means algorithm initializes cluster centroids and assigns observations to the nearest centroid, recomputing centroids until convergence.
3. K-means clustering is commonly used for applications like machine learning, data mining, and image segmentation due to its efficiency, though it is sensitive to initialization and assumes spherical clusters.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
The document discusses various decision tree learning methods. It begins by defining decision trees and issues in decision tree learning, such as how to split training records and when to stop splitting. It then covers impurity measures like misclassification error, Gini impurity, information gain, and variance reduction. The document outlines algorithms like ID3, C4.5, C5.0, and CART. It also discusses ensemble methods like bagging, random forests, boosting, AdaBoost, and gradient boosting.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
The document discusses the different phases of a compiler: lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. It explains that a compiler takes source code as input and translates it into an equivalent language. The compiler performs analysis and synthesis in multiple phases, with each phase transforming the representation of the source code. Key activities include generating tokens, building a syntax tree, type checking, generating optimized intermediate code, and finally producing target machine code. Symbol tables are also used to store identifier information as the compiler runs.
Data Mining - Classification Of Breast Cancer Dataset using Decision Tree Ind...Sunil Nair
The document summarizes research on classifying breast cancer datasets using decision trees. The researchers used a Wisconsin breast cancer dataset containing 699 instances with 10 attributes plus a class attribute. They preprocessed the data to handle missing values, compared various classification methods, and achieved the best accuracy of 97% using decision trees with attribute selection. Issues addressed included unbalanced classes and future work proposed methods like clustering and multiple classifiers to further improve accuracy.
This document provides information about the CS416 Compiler Design course, including the instructor details, prerequisites, textbook, grading breakdown, course outline, and an overview of the major parts and phases of a compiler. The course will cover topics such as lexical analysis, syntax analysis using top-down and bottom-up parsing, semantic analysis using attribute grammars, intermediate code generation, code optimization, and code generation.
The document discusses decision theory and decision trees. It introduces decision making under certainty, risk, and uncertainty. It defines elements related to decisions like goals, courses of action, states of nature, and payoffs. It also discusses concepts like expected monetary value, expected profit with perfect information, expected value of perfect information, and expected opportunity loss. Examples are provided to demonstrate calculating these metrics. Finally, it provides an overview of how to construct a decision tree, including defining the different node types and how to calculate values within the tree.
The compilation process consists of multiple phases that each take the output from the previous phase as input. The phases are: lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation.
The analysis phase consists of three sub-phases: lexical analysis, syntax analysis, and semantic analysis. Lexical analysis converts the source code characters into tokens. Syntax analysis constructs a parse tree from the tokens. Semantic analysis checks that the program instructions are valid for the programming language.
The entire compilation process takes the source code as input and outputs the target program after multiple analysis and synthesis phases.
The lesson covers lists in Python including an introduction to lists, list operations like enumeration and mutability, and challenges using lists like creating a contacts list program and times table application. Various list methods are demonstrated and discussed such as sorting, appending, searching by index, and removing elements from a list. The anatomy and uses of lists in computing and programming are explored.
1. Personal interaction and building relationships are often more effective than evidence alone in influencing decision makers.
2. Using intermediaries like staff, secretaries, and influential contacts can help get your message heard. Developing networks of people is important.
3. The media can help bring issues to politicians' attention and legitimize research, but managing media relations carefully is important.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
An influence line shows the variation of shear, moment, reaction, or member stress in a structure due to a moving unit load. It is constructed by plotting the value of the specific function as a unit load is moved along the structure.
Influence lines for determinate structures are always straight lines. To construct an influence line, the effect of a unit load is observed as it is moved along the span, and the controlling ordinates are calculated and plotted.
The Muller-Breslau principle states that the ordinates of an influence line are proportional to the deflected shape of the structure if the capacity for a specific force is removed and an equivalent displacement is introduced.
Este documento describe diferentes técnicas para modelar la incertidumbre en la toma de decisiones, incluyendo diagramas de influencia. Explica cómo medir y representar gráficamente la incertidumbre, y provee ejemplos de cómo usar diagramas de influencia para evaluar decisiones que involucran riesgo e incertidumbre.
This document describes the simulated annealing algorithm. It begins with an introduction that outlines the document. It then provides a formal definition of simulated annealing, explaining how it is analogous to the way metals cool and form crystalline structures. The core of the document describes the simulated annealing algorithm, including initializing a solution, evaluating solutions, making random changes, applying a criterion to accept or reject changes, and gradually lowering the temperature. It provides an example of applying the algorithm to the 40 queens puzzle. It also discusses tuning the algorithm by setting the initial and final temperatures. In conclusion, it covers the advantages, disadvantages, and applications of simulated annealing.
The document discusses the Analytical Hierarchy Process (AHP), a decision-making tool used to prioritize alternatives and build an additive value function. AHP attempts to mirror the human decision process by structuring decisions into a hierarchy and using pairwise comparisons. It allows for multiple decision makers and can provide weights for criteria, though inconsistencies may arise and it is controversial due to issues like rank reversal. The document outlines the AHP procedure and discusses measuring and addressing consistency in pairwise comparisons.
The document discusses three programming language translators: assemblers translate assembly language into machine code, compilers translate high-level languages into executable object code, and interpreters execute instructions one at a time without producing an executable file. Assemblers convert mnemonics to machine language equivalents and assign addresses, compilers check syntax and generate all code at once, and interpreters check keywords and convert instructions individually to machine code.
Simulated Annealing - A Optimisation TechniqueAUSTIN MOSES
Simulated annealing is a global optimization technique inspired by the physical process of annealing in solids. It can find the global minimum of a cost function by slowly cooling the system. At each temperature, the algorithm accepts random moves to neighboring solutions with a probability based on the change in cost and current temperature. This allows occasionally moving to higher-cost solutions and avoids getting stuck in local minima. While slower than local search methods, simulated annealing is more likely to find the global optimum solution over multiple iterations as the temperature decreases.
Software Engineering- ERD DFD Decision Tree and TableNishu Rastogi
Second half of Unit 2 of BCA 401 as per Invertis University, Syllabus
It includes introduction to ERD, DFD, Decision Tree and Table with examples and exercise.
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
The document provides an overview of the Analytic Hierarchy Process (AHP), a decision-making tool developed by Thomas Saaty in the 1970s. It discusses the background, development, methodology, applications, advantages, and disadvantages of AHP. Examples of organizations that use AHP for decision making are also given, along with a case study on how AHP was used to evaluate candidates for an entrepreneur award in Terengganu, Malaysia.
The document describes the Analytic Hierarchy Process (AHP), which is a structured technique for organizing and analyzing complex decisions. AHP involves constructing a hierarchy of criteria and alternatives, then making pairwise comparisons between elements to determine their relative importance. These comparisons are used to calculate weights for criteria and priorities for alternatives. The document provides an example of using AHP to select a car based on style, reliability, and fuel economy criteria. It also outlines the steps to determine criteria weights, alternative priorities, and consistency ratios in AHP.
The document describes the Analytic Hierarchy Process (AHP), a technique for structuring complex decisions. It involves breaking the decision down into a hierarchy, then using pairwise comparisons to determine the relative importance of criteria and alternatives. The example provided illustrates Judy Grim using AHP to choose a new computer system. She identifies hardware, software, and vendor support as criteria. Alternatives are three computer systems. Pairwise comparisons assign weights to criteria and ratings to alternatives on each criterion. The highest rated alternative is selected.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Efficient classification of big data using vfdt (very fast decision tree)eSAT Journals
Abstract
Decision Tree learning algorithms have been able to capture knowledge successfully. Decision Trees are best considered when
instances are described by attribute-value pairs and when the target function has a discrete value. The main task of these
decision trees is to use inductive methods to the given values of attributes of an unknown object and determine an
appropriate classification by applying decision tree rules. Decision Trees are very effective forms to evaluate the performance
and represent the algorithms because of their robustness, simplicity, capability of handling numerical and categorical data,
ability to work with large datasets and comprehensibility to a name a few. There are various decision tree algorithms available
like ID3, CART, C4.5, VFDT, QUEST, CTREE, GUIDE, CHAID, CRUISE, etc. In this paper a comparative study on three of
these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very
Fast Decision Tree has been made. An empirical study has been conducted to compare C4.5 and VFDT in terms of accuracy
and execution time and various conclusions have been drawn.
Key Words: Decision tree, ID3, C4.5, VFDT, Information Gain, Gain Ratio, Gini Index, Over−fitting.
Data Mining Concepts and Techniques.pptRvishnupriya2
This document discusses classification techniques in data mining, including decision trees. It covers supervised vs. unsupervised learning, the classification process, decision tree induction using information gain and other measures, handling continuous attributes, overfitting, and tree pruning. Specific algorithms covered include ID3, C4.5, CART, and CHAID. The goal of classification and how decision trees are constructed from the training data is explained at a high level.
Data Mining Concepts and Techniques.pptRvishnupriya2
This document discusses classification techniques for data mining. It covers supervised and unsupervised learning methods. Specifically, it describes classification as a two-step process involving model construction from training data and then using the model to classify new data. Several classification algorithms are covered, including decision tree induction, Bayes classification, and rule-based classification. Evaluation metrics like accuracy and techniques to improve classification like ensemble methods are also summarized.
This document provides an overview of classification techniques for machine learning. It defines classification as predicting categorical class labels based on a training set. Decision tree induction and Bayes classification methods are described as common classification approaches. Decision trees are constructed recursively to partition data based on attribute tests. Information gain, gain ratio, and Gini index are discussed as measures for selecting the best attributes to test at each node. The naïve Bayes classifier is introduced as a simplified Bayesian approach based on conditional independence assumptions between attributes.
This document provides an overview of classification techniques in machine learning. It discusses:
- The process of classification involves model construction using a training set and then applying the model to classify new data.
- Supervised learning aims to predict categorical labels while unsupervised learning groups data without labels.
- Popular classification algorithms covered include decision trees, Bayesian classification, and rule-based methods. Attribute selection measures, model evaluation, overfitting, and tree pruning are also discussed.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (link resides outside ibm.com) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.
Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.
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Machine Learning vs. Deep Learning vs. Neural Networks
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (link resides outside ibm.com).
Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs,
This document discusses decision tree induction and attribute selection measures. It describes common measures like information gain, gain ratio, and Gini index that are used to select the best splitting attribute at each node in decision tree construction. It provides examples to illustrate information gain calculation for both discrete and continuous attributes. The document also discusses techniques for handling large datasets like SLIQ and SPRINT that build decision trees in a scalable manner by maintaining attribute value lists.
Implementation of Improved ID3 Algorithm to Obtain more Optimal Decision Tree.IJERD Editor
Decision tree learning is the discipline to create a predictive model to map the different items in the
set and respective target values and associate them in a way that is true to every element. This concept is used in
statistics, data mining and machine learning due to its simple and effectiveness.
Among the various strategies available to construct the decision trees ID3 is one of the simplest and
most widely used decision tree algorithm, but ID3 algorithm gives more importance to attributes having
multiple values while selecting node. This major shortcoming affects the accuracy of decision tree. In this paper
we are proposing improvement in ID3 algorithm using association function (AF). The Experimental result
shows improved ID3 algorithm can overcome shortcomings of ID3 which will also improve the accuracy of ID3
algorithm.
Perfomance Comparison of Decsion Tree Algorithms to Findout the Reason for St...ijcnes
Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, rule mining can be applied on the educational data for predicting the students behavior. This paper focuses on finding thesuitablealgorithm which yields the best result to find out the reason behind students absenteeism in an academic year. The first step in this processis to gather students data by using questionnaire.The datais collected from 123 under graduate students from a private college which is situated in a semirural area. The second step is to clean the data which is appropriate for mining purpose and choose the relevant attributes. In the final step, three different Decision tree induction algorithms namely, ID3(Iterative Dichotomiser), C4.5 and CART(Classification and Regression Tree)were applied for comparison of results for the same data sample collected using questionnaire. The results were compared to find the algorithm which yields the best result in predicting the reason for student s absenteeism.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
The document discusses decision tree learning algorithms. It explains that a decision tree is a tree-structured model that tests attributes to predict an output. The ID3 algorithm selects the attribute with the highest information gain at each step and recursively builds the tree. Different algorithms like ID3, C4.5 and CART are described, along with measures used to select nodes like entropy, information gain, and Gini index. It also discusses handling numeric data and provides references for further information.
The document discusses attribute selection measures used in decision tree algorithms like ID3. It describes three popular measures - information gain, gain ratio, and Gini index. Information gain selects the attribute that minimizes the expected number of tests needed to classify tuples. Gain ratio normalizes information gain to account for attributes with many values. Gini index measures the impurity of a data partition based on the probability of misclassifying tuples. The document also discusses pruning decision trees to reduce overfitting, and issues like handling continuous attributes.
This document summarizes key concepts from Chapter 8 of the textbook "Data Mining: Concepts and Techniques". It discusses classification, which predicts categorical class labels, as a supervised learning technique. It covers decision trees for classification, which select attributes to split data recursively based on measures like information gain. It also discusses evaluating and improving classification models to avoid overfitting, and scaling classification to large databases.
This document summarizes key concepts from Chapter 8 of the textbook "Data Mining: Concepts and Techniques". It discusses classification, which predicts categorical class labels. The two main processes are model construction using a training set, and then using the model to classify new data. Decision trees, Naive Bayes, and rule-based classification algorithms are covered. Metrics for evaluating models like accuracy and techniques for improving accuracy like ensemble methods are also summarized.
The document discusses decision tree models in machine learning. It begins by defining key terms like decision nodes, branches, and leaf nodes. It then explains how decision trees are built in a top-down manner by recursively splitting the training data based on selected attributes. The document also covers different algorithms for building decision trees like ID3, C4.5, and CART. It discusses measures used for attribute selection like information gain, gain ratio, and Gini index. Finally, it provides an example of how to build a decision tree to classify whether to play tennis based on weather attributes.
The document discusses decision tree construction algorithms. It explains that decision trees are built in a top-down, recursive divide-and-conquer approach by selecting the best attribute to split on at each node, creating branches for each possible attribute value. It also discusses different splitting criteria like information gain and Gini index that are used to determine the best attribute to split on. Finally, it mentions several decision tree algorithms like ID3, C4.5, CART, SLIQ and SPRINT that use these concepts.
This document discusses decision trees, which are supervised learning algorithms used for both classification and regression. It describes key decision tree concepts like decision nodes, leaves, splitting, and pruning. It also outlines different decision tree algorithms (ID3, C4.5, CART), attribute selection measures like Gini index and information gain, and the basic steps for implementing a decision tree in a programming language.
Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
This is an overview of my career in Aircraft Design and Structures, which I am still trying to post on LinkedIn. Includes my BAE Systems Structural Test roles/ my BAE Systems key design roles and my current work on academic projects.
3. Decision tree Induction
Training dataset should be class-labelled for learning of decision.
A decision-tree represent rules and it is very popular tool for classification
and prediction
Rules are easy to understand and can be directly used in SQL to retrieve
records
There are many algorithm to build decision tree:
o ID3(Iterative Dichotomiser 3)
o C4.5
o CART(Classification and Regression Tree)
o CHAID(Chi-squared Automatic Interaction Detector)
4. Decision tree has tree type structure which has leaf nodes and decisions
node.
A leaf node is the last node of each branch.
A decision node is the node of tree which has leaf node or sub-tree.
Decision tree Representation
5. Attribute for decision tree are selected by one of the following method:
1. Gini index(IBM IntelligentMiner)
2. Information Gain(ID3/C4.5)
3. Gain ratio
Attribute are categories into two part:
1. Attribute whose domain is numerical are called numerical attribute
2. Attribute whose domain is non-numerical are called categorical attribute.
Attribute Selection
6. It can be adapted for categorical attributes
Uses in CART, SPRINT and IBM’s Intelligent miner System
Formula for Gini index is
For a valued attribute, the attribute providing the smallest gini is chosen to
split the node.
Gini index
7. It can be adapted for continuous-valued attribute as well as categorical
data.
Attribute which has highest information gain is selected for split.
If Si contain pi examples of P and ni examples of N, the entropy to classify
object is
Information gain
8. Expected amount of information needed to assign a class to a randomly
drawn object in S
Calculate information gain i.e. gain(A) : Measure reduction in entropy
achieved because of split.
𝑮𝒂𝒊𝒏 𝑨 = 𝑰 𝒑, 𝒏 − 𝑬(𝑨)
Entropy
9.
10. Decision trees are able to generate understandable rules
Perform classification without requiring much computation
Handle categorical as well as continuous variable
Provide clear induction of which fields are most important
Strength of decision tree
Weakness of decision tree
Not suitable for prediction of continuous attribute
Computationally expensive to train
11. Two types
1. Prepruning
Start pruning in the beginning while building the tree itself
Stop the tree construction in early stage
Avoid splitting node by checking the threshold
2. Postpruning
Build the tree then start pruning
Use different set of data than training dataset to get best pruned tree
Tree Pruning
12. A Training set
Age Car Type Risk
23 Family High
17 Sports High
43 Sports High
68 Family Low
32 Truck Low
20 Family High
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