Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
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.
No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
This document provides an overview of Naive Bayes classification. It begins with background on classification methods, then covers Bayes' theorem and how it relates to Bayesian and maximum likelihood classification. The document introduces Naive Bayes classification, which makes a strong independence assumption to simplify probability calculations. It discusses algorithms for discrete and continuous features, and addresses common issues like dealing with zero probabilities. The document concludes by outlining some applications of Naive Bayes classification and its advantages of simplicity and effectiveness for many problems.
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 document discusses decision tree algorithms C4.5 and CART. It explains that ID3 has limitations in dealing with continuous data and noisy data, which C4.5 aims to address through techniques like post-pruning trees to avoid overfitting. CART uses binary splits and measures like Gini index or entropy to produce classification trees, and sum of squared errors to produce regression trees. It also performs cost-complexity pruning to find an optimal trade-off between accuracy and model complexity.
Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.
Genetic algorithms are inspired by Darwin's theory of natural selection and use techniques like inheritance, mutation, and selection to find optimal solutions. The document discusses genetic algorithms and their application in data mining. It provides examples of how genetic algorithms use selection, crossover, and mutation operators to evolve rules for predicting voter behavior from historical election data. The advantages are that genetic algorithms can solve complex problems where traditional search methods fail, and provide multiple solutions. Limitations include not guaranteeing a global optimum and variable optimization times. Applications include optimization, machine learning, and economic modeling.
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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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.
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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.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
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.
The document discusses decision trees, which are a type of predictive modeling that can be used for segmentation. It provides examples of how to segment a population of customers into subgroups based on attributes like employment status and income. The key aspects of decision trees covered include how they are constructed from a root node down to leaf nodes, different algorithms for building decision trees, measures for determining the best attributes to split on like information gain, and techniques for validating and pruning trees to avoid overfitting.
1) Decision trees can be used to model decision problems that involve uncertainty about the future. They illustrate the potential outcomes of decisions and their probabilities.
2) A decision tree for Thompson Lumber Company considering expanding into backyard sheds shows two potential market states and three course of action options.
3) Expected monetary value (EMV) is calculated for each option to determine the best decision without knowing the market state beforehand. Additional market testing could provide more information.
The document describes the C4.5 algorithm for building decision trees. It begins with an overview of decision trees and the goals of minimizing tree levels and nodes. It then outlines the steps of the C4.5 algorithm: 1) Choose the attribute that best differentiates training instances, 2) Create a tree node for that attribute and child nodes for each value, 3) Recursively create subordinate nodes until reaching criteria or no remaining attributes. An example applies these steps to build a decision tree to predict customers' responses to a life insurance promotion using attributes like age, income and insurance status.
This presentation covers Decision Tree as a supervised machine learning technique, talking about Information Gain method and Gini Index method with their related Algorithms.
Decision tree powerpoint presentation templatesSlideTeam.net
The document presents a decision tree diagram that can be used to evaluate and prioritize ideas using symbols. The diagram shows a basic decision tree structure with text placeholders throughout for customization. The decision tree can be edited and customized fully in PowerPoint by changing colors, sizes, orientations and text of any part of the tree.
This document presents an example decision problem to demonstrate decision tree analysis. It describes three potential decisions - expand, maintain status quo, or sell now - under two possible future states, good or poor foreign competitive conditions. It then outlines the steps to analyze the problem: 1) determine the best decision without probabilities using various criteria, 2) determine the best decision with probabilities using expected value and opportunity loss, 3) compute the expected value of perfect information, and 4) develop a decision tree showing expected values at each node.
This document discusses decision making under risk and uncertainty for capacity planning. It describes different approaches for dealing with uncertainty such as maximin, maximax, Laplace, and minimax regret. The expected value and expected value of perfect information are calculated. The expected value is the highest for the medium facility alternative using the expected monetary value criterion and minimax regret approach. The expected value of perfect information is $1.7 million, which is the difference between having perfect information versus making the decision under risk.
This document discusses decision trees and their use in predictive modeling. It provides an example of using a decision tree to predict credit ratings. The decision tree splits the data into nodes based on variables like checking accounts, savings accounts, and duration. Each node shows the percentage of good and bad credit ratings, with deeper nodes having higher percentages. Decision trees allow targeting subsets of a population that have higher response rates to improve outcomes.
The document discusses decision trees, which are diagrams that illustrate decisions and their potential consequences. It provides examples of decision trees used by two companies - Manly Plastics and Vine Desserts - to analyze decisions about new product development and business location selection. It also discusses key concepts in decision trees, including decision nodes, chance nodes, expected value calculations, and how decision trees can be used for regression and survival analysis involving continuous or time-to-event outcomes.
When assessing the possibility to in- or outsource often matrixes are used. The two axes of a matrix are however hardly sufficient to capture the complexity of a sourcing decision. More effective are so called sourcing decision trees. This presentations outlines one of them.
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 overview of problem solving and decision making for supervisors. It discusses identifying problems in the workplace, using problem solving models like the 6 step approach and fishbone analysis to determine root causes. Effective problem solvers are confident, flexible, and learn from mistakes. Decision making involves defining problems, gathering information from stakeholders, developing alternatives, and selecting the best option. Involving teams in decisions improves morale but takes more time. Supervisors must determine when individual or group decisions are most appropriate.
The document discusses decision tree learning and the ID3 algorithm. It covers topics like decision tree representation, entropy and information gain for selecting attributes, overfitting, and techniques to avoid overfitting like reduced error pruning. It also discusses handling continuous values, missing data, and attributes with many values or costs in decision tree learning.
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 Analysis for statistical tool. The deck provides understanding on the Decision Analysis.
It provides practical application and limited theory. Will be useful for MBA students.
This document discusses decision trees, which are tools that model decisions and their potential outcomes. A decision tree uses a tree-like structure to display an algorithm and help identify the most optimal strategy to reach a goal. Decision trees are commonly used in operations research and decision analysis. They have advantages of being simple to understand and interpret, and can provide valuable insights even with limited hard data by leveraging expert knowledge. An example decision tree is provided to illustrate modeling weekend activity choices based on weather, finances, and parental visits.
The document outlines an 8-step decision making process for selecting a soft drink:
1) Identify the problem
2) Identify decision criteria like brand, taste, price, packaging, and color
3) Allocate weights to the criteria
4) Develop alternatives like Coca Cola, Sprite, Pepsi, etc.
5) Analyze the alternatives based on the criteria
6) Select the alternative with the highest total score
7) Implement the decision
8) Evaluate the effectiveness of the decision
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.
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
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.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
A decision tree is a guide to the potential results of a progression of related choices. It permits an individual or association to gauge potential activities against each other dependent on their costs, probabilities, and advantages. They can be utilized either to drive casual conversation or to outline a calculation that predicts the most ideal decision scientifically.
The document discusses classification and prediction using decision trees. It begins by defining classification as predicting categorical labels from data, such as predicting if a loan applicant is "safe" or "risky". Prediction involves predicting continuous or ordered values, such as how much a customer will spend. The document then discusses how decision trees perform classification by recursively splitting the data into purer subsets based on attribute values, with leaf nodes representing class labels. Information gain is used as the splitting criterion to select the attribute that best splits the data. Finally, it notes that attributes with many values can bias decision trees towards overfitting.
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 discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting and model evaluation, discussing metrics, methods of evaluation like cross validation, and comparing models.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting, model evaluation methods, and comparing model performance.
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.
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,
Machine learning algorithms can learn through supervised, unsupervised, or reinforcement learning. Supervised learning involves providing labeled examples to learn a function that maps inputs to outputs. Unsupervised learning identifies hidden patterns in unlabeled data. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment. Machine learning has applications in areas like computer vision, natural language processing, medical diagnosis, and more.
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 involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
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.
The document provides an overview of decision tree learning algorithms:
- Decision trees are a supervised learning method that can represent discrete functions and efficiently process large datasets.
- Basic algorithms like ID3 use a top-down greedy search to build decision trees by selecting attributes that best split the training data at each node.
- The quality of a split is typically measured by metrics like information gain, with the goal of creating pure, homogeneous child nodes.
- Fully grown trees may overfit, so algorithms incorporate a bias toward smaller, simpler trees with informative splits near the root.
This document outlines a project to develop an automated mobile payment gateway that connects a mobile wallet, payment gateway server, and client server. It aims to minimize synchronization time for transactions by having an Android service installed on a phone that sends payment data via SMS to the payment gateway server. The server then transfers the data to the client server, and sends confirmation back. A cron job is used to resend any unsynchronized data to ensure successful transaction verification without manual user confirmation. The goal is to create a faster, more automated process for mobile payments compared to existing wallet systems.
µIP (micro IP) is very small TCP/IP stack. Open source, widely used memory-constrained embedded product. This protocol stack intended for use with tiny 8- and 16-bit microcontrollers.
Developed by Adam Dunkels
This document outlines the admin panel interface for a software called POZ that allows managing goods, sales, and reports. The interface includes options to add goods with name, price, type, and serial number. Goods can be managed with an edit/delete function. Sales can be tracked by entering product name, serial ID, quantity, and price. Reports can be generated either daily, monthly, or by individual product.
This document proposes a remote collaboration system called ShowMe that allows an expert and novice user to collaborate immersively using gestural communication. The expert sees the novice's field of view and can share their own hands' positions in real-time using depth sensors. ShowMe is implemented using Oculus Rifts and Unity3D to share audio, video and hand tracking data between two laptops over a network. Future work aims to improve screen resolution and implement transparent HMDs.
The document outlines the features and functionality of a school management system. It describes panels for finance/accounts, administration, teachers, students, and the library manager. It also lists functions such as fee collection, pay slip generation, exam and class scheduling, transportation management, academic calendars, library management, and student, teacher and staff profiles. Administrative features include user account management and customizing the school logo and name.
The Arena is active protection system developed at Russia's Kolomna-based Engineering Design Bureau for the purpose of protecting important places. The Arena system was primarily designed to defeat threats. It uses a Doppler radar to detect incoming warheads and destroy it by throwing projectile. By the array scan technology of image processing now is easy to detect the area of an object. Most of the war threads are in common format. So it was easy to detect any object nature. By using picture element information, this system can sense an object. The main purpose of this research is to build a cost effective Arena Active Protection System that is less costly than current system. The purpose of this research work is to invent a method to avoid Doppler radar which is used in current system and use pair of phototransistor which is more cost effective then Radar.
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides re sizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.
Implementation of RSA Algorithm for Speech Data Encryption and DecryptionMd. Ariful Hoque
An efficient implementation of RSA algorithm for speech data encryption and decryption. At first, five hundred Bangla speech words were recorded from six different speaker and stored as RIFF (.wav) file format. Then our developed program was used to extract data from these words and this data were stored in a text file as integer data. Finally, we used our implemented program to encrypt and decrypt speech data.
Animal Classification Based on 5 Kingdom Division.pdfSELF-EXPLANATORY
This pdf is about the Animal Classification Based on 5 Kingdom Division.
For more details visit on YouTube; @SELF-EXPLANATORY; https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
The Black Soldier Fly (Hermetia illucens) is an environmentally beneficial insect known for its impressive ability to decompose organic waste efficiently. Native to the Americas, this fly has gained global attention due to its larvae's ability to convert waste into valuable products. The larvae consume a wide range of organic materials, including food scraps, manure, and agricultural waste, significantly reducing the volume of waste and producing nutrient-rich frass, which can be used as a high-quality organic fertilizer. Additionally, the larvae themselves are rich in proteins and fats, making them an excellent source of animal feed, particularly for poultry, fish, and pigs. The use of Black Soldier Fly larvae in waste management and sustainable farming practices presents an innovative solution to some of the pressing environmental challenges related to waste disposal and food production. This insect not only helps in managing waste but also contributes to the circular economy by transforming waste into valuable resources.
Another Story of Pattern Recognition.
Is Deep Learning the only way?International Computer Vision Summer School (ICVSS) 2016 presentation held in Sicily on 22nd July 2016.
Solar Coronal Heating by Kinetic Alfvén WavesSérgio Sacani
The utilization of the Cairns distribution serves as a vital tool for characterizing the nonthermal attributes
commonly observed in space plasmas. In these intricate plasma environments, extensive measurements have been
conducted to monitor the fluctuations inherent in the perturbed electromagnetic (EM) field and the associated
Poynting flux, specifically concerning kinetic Alfvén waves (KAWs). Traditionally, these fluctuations have been
attributed to gyroradius correction terms within the framework of Maxwellian distributed plasmas. However, our
study introduces an innovative perspective grounded in kinetic theory coupled with the Cairns distribution, adept at
encapsulating the nonthermal nuances characterized by the index parameter Λ. Within the domain of the solar
corona, our investigation centers on the perturbed EM field ratios and the Poynting flux of KAWs, with a
foundation in the Cairns distribution function. It is noteworthy that the perpendicular components, although
deemed less significant due to the dominance of k⊥ over k∥, remain unquantified regarding their relative
insignificance. Similarly, the exploration of the imaginary part of the normalized EM field ratio has been a
relatively understudied domain. Furthermore, we delve into the nuanced assessment of the power rate Ix/Iz
characterizing the perpendicular and parallel normalized Poynting fluxes (Sx and Sz). Intriguingly, we discern that
large values of Λ, compared to their Maxwellian counterparts, manifest advantageous attributes, particularly
concerning the energization of the plasma over extended distances along the ambient magnetic field lines. The
analytical insights gleaned from this study find practical application in understanding phenomena within the solar
atmosphere, particularly shedding light on the significant role played by nonthermal particles in the observed
heating processes.
Role of bio fertilizers in sustainable organic agricultural developmentAyanKoley2
In agriculture, bio-fertilizers are the best modern tools. It is the gift of the modern agricultural science. Human population is increased day by day in every year. So it is the major threats to the food security for the restricted agricultural lands. For the great demands of food we should enhanced the agricultural productivity significantly. So with the too much uses of chemical fertilizers production of crops, ecosystem and the human health could be damaged. Moreover the soil fertility, air, soil and water pollution could be developed. Bio-fertilizers are the major gift of nature which is a replacement of the chemical fertilizers. It contains micro-organisms to encourage the adequate nutrients supply to the host plant and enhance their growth and development. It plays an important role in atmospheric nitrogen fixation and maintaining the long term soil fertility and sustainable for the production of crops.
Now a days the population of the world is 7.9 Billion and 1.37 Billion people are contained alone India. This population number is increasing day by day. In 2011, according to 15th Census of India, 17.64% population growth is observed where rural population is 68.84%. So, human population is growing and it demands usual agriculture for the requirements of food. The demands of food make the farmers to depend on the pesticides and chemical fertilizers to get a greater yield.[1] In the industries chemical fertilizers are made up of many types of nutrients like nitrogen, phosphorus, potassium etc. These fertilizers have many harmful effects on roots of the plants, soil, ground water and water bodies. The problems may arise like weakening of roots, increase of the incidence of disease, acidification of soil [2] and eutrophication of the ground water and the water bodies.[3] Nitrates are the nutrients which leach to the ground water and cause a syndrome that is Blue Baby Syndrome or Acquired Methemoglobinemia.[4] There is a large impact of these chemicals on future generations. So now a days eco-friendly approaches are very much popular for maintaining the biosafety level. Due to the stringent regulation on the use of chemical fertilizers Latin American and European countries are the consumers of biofertilizers who actually replaced chemical fertilizers by biofertilizers.[5]
Organic farming is very much important as it has an area of interest globally on the increasing demand for the healthy and safer food, long term sustainability and the environmental pollution which is associated with the indiscriminate use of the chemicals like agrochemicals.[6] Bio-fertilizers are very much significant components of organic farming. Bio-diversity can be defined as the substances containing the living micro-organisms which is colonize to the rhizosphere of the plants and promote the growth of plants with increasing the availability of the primary nutrients to the specific crop to the soils, seeds or plant surfaces.[7] It is prepared by latent or live cells.
5S and 45S rDNA monomer organization: lengths, variation and interruption in tandem arrays from Musaceae species
Authors
Pat Heslop-Harrison1,2, Qing Liu2 , Ziwei Wang2, Trude Schwarzacher1,2
Affiliations
1 University of Leicester, Leicester, United Kingdom 2 South China Botanical Garden, Guangzhou, China
Abstract
Long, single-molecule DNA sequencing shows the organization and structures of rDNA monomers in tandem repeats. Short reads of both 5S and 45S rDNA collapse the arrays during assembly, while older BAC sequences suffer from chaemerism and assembly artefacts. Far from being a continuous array of monomers, we find short deletions, insertions or interruptions in the arrays. Full-length retroelements are found at variable points within some 45S and 5S monomers in the arrays, and there are occasional insertions of uncharacterized sequences. Within monomers, both deletions and short duplications are found. Similar rearrangements have been found in multiple, non-identical, reads, giving evidence for homogenization through unequal crossing-over (and hence duplication of segments of the arrays). The 'starts' of the arrays have been characterized with flanking sequences. Musaceae provides a good model for the comparative study of the rDNA arrays, with long reads available from multiple species, variable chromosome numbers and evolutionary movement of rDNA between chromosomes, independent of other genes. The rDNA is very variable between species, many with one pair sites of 45S rDNA, representing 1% of all the DNA, to Musa beccarii with 3 sites and 5% of the DNA. Monomer lengths are also variable, with the typical length around 400bp found for most 5S monomers but 1056bp in Ensete. The detailed characterization of the arrays shows evolutionary mechanisms and diversity of the ribosomal DNA arrays. Further information and references are given at www.molcyt.org .
IBC2024 Madrid International Botanical Congress XX XXIBC
This presentation by Dr.S.Anandhi, Assistant Professor, SRMCAS provides you brief information on what is Soil Sterilization, the need for Soil Sterilization and about the process involved in different methods of soil sterilization.
The below presentation by Dr.S.Anandhi, Assistant Professor, SRMCAS elaborates about Special Vegetative Structure and types of shoot & root modifications.
This pdf is about the Eight Millennium Development Goals (MDGs).
For more details visit on YouTube; @SELF-EXPLANATORY; https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
2. Introduction.
Decision Tree Terms.
Example.
Constructing A decision Tree.
Calculation Of Entropy.
Information Gain.
Gini Impurity.
Termination Criteria.
Mathlab Example.
Implementations.
Advantage
Limitation
Conclusion
3. Decision tree learning is the construction of a decision tree from class-
labeled training tuples.
A decision tree is model of decisions and their possible consequences.
It Includes chance event outcomes, resource costs, and utility.
Its follow top down approach.
Decision trees classify instances by sorting them down the tree from the
root to some leaf node, which provides the classification of the instance
5. Yes
Color = Green ?
Size = Big ? Color = Yellow ?
Size =medium?Watermelon
Apple Grape
Shape = Round? Size = Small ?
Taste = sweet? Apple
Yes No
Yes No
Cherry Grape
Size = Big?
Grape Lemon
Banana
Yes
YesYes
Yes
Yes
No
No
No
No
No
6. There are many specific decision-tree algorithms-
ID3
C4.5
CART
CHAID
MARS
7. Which attribute to choose?
◦ Information Gain
ENTROPY
Where to stop?
◦ Termination criteria
8. Different algorithms use different metrics for measuring best.
These generally measure the homogeneity of the target variable within
the subsets.
Some examples are given in the next slides.
9. ◦ Entropy is a measure of uncertainty in the data
Entropy(S) = ∑(i=1 to l)-|Si|/|S| * log2(|Si|/|S|)
S = set of examples
Si = subset of S with value vi under the target attribute
l = size of the range of the target attribute
10. Used by the ID3, C4.5 and C5.0 tree-generation algorithms.
Information gain is based on the concept of entropy from information
theory.
Here , fi = fraction of items
m = Items
11. Used by the CART (classification and regression tree).
It measure incorrectly labeled of randomly chosen element.
Gini impurity can be computed by summing the probability of each item
being chosen times the probability of a mistake in categorizing that item.
It reaches its minimum (zero) when all cases in the node fall into a single
target category.
To compute Gini impurity for a set of items, suppose-
Let f be the fraction of items labeled with value i in the set.
12. All the records at the node belong to one class
A significant majority fraction of records belong to a single class
The segment contains only one or very small number of records
The improvement is not substantial enough to warrant making the split.
13. Create a classification decision tree for Fisher's iris data:
load fisheriris;
t = classregtree(meas,species,...
'names',{'SL' 'SW' 'PL' 'PW'})
view(t)
14. t =
Decision tree for classification
if PL<2.45 then node 2 elseif PL>=2.45 then node 3 else setosa
class = setosa
if PW<1.75 then node 4 elseif PW>=1.75 then node 5 else versicolor
if PL<4.95 then node 6 elseif PL>=4.95 then node 7 else versicolor
class = virginica
if PW<1.65 then node 8 elseif PW>=1.65 then node 9 else versicolor
class = virginica
class = versicolor
class = virginica
16. In data mining software.
Several examples include Salford Systems CART, IBM SPSS , KNIME,
Microsoft SQL Server, and scikit-learn.
17. Decision-tree learners can create over-complex trees.
There are concepts that are hard to learn because decision trees do not
express them easily, such as XOR, parity or multiplexer problems.
When there are more records and very less number of attributes/features.
18. Simple to understand and interpret.
Requires little data preparation.
Able to handle both numerical and categorical data.
Performs well with large datasets.
19. Decision tree learning is one of the predictive modeling approaches used
in statistics, data mining and machine learning.
In our example section we saw a classification tree.
Where the target variable can take a finite set of values.
In Mathlab example section we saw regression trees.
Where the target variable can take continuous values (typically real
numbers).
20. 1. Decision tree learning[Online].
Available:http://en.wikipedia.org/wiki/Decision_tree_learning
2. Classregtree[Online].
Available:http://www.mathworks.com/help/stats/classregtree.html
3. Richard O.Duda, Peter E. Hart, David G. Stok. Pattern Classification.
Second Edition
4. Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and
Regression Trees. Boca Raton, FL: CRC Press, 1984.