1) The 1R algorithm generates a one-level decision tree by considering each attribute individually and assigning the majority class to each branch. It chooses the attribute with the minimum classification error.
2) Naive Bayes classification assumes attributes are independent and calculates the probability of each class using Bayes' rule. It handles missing and numeric attributes.
3) Decision tree algorithms like ID3 use a divide-and-conquer approach, recursively splitting the data on attributes that maximize information gain or gain ratio at each node.
4) Rule-based algorithms like PRISM generate rules to cover instances of each class sequentially, maximizing the ratio of correctly covered to total covered instances at each step.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
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.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Sequential collection of elements of the same type
used to store a collection of data
consist of contiguous memory locations
lowest address corresponds to the first element and the highest address to the last element.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
It was prepared under guidance of Prof. Sandra Kubler.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
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.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Sequential collection of elements of the same type
used to store a collection of data
consist of contiguous memory locations
lowest address corresponds to the first element and the highest address to the last element.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
This presentation was prepared as part of the curriculum studies for CSCI-659 Topics in Artificial Intelligence Course - Machine Learning in Computational Linguistics.
It was prepared under guidance of Prof. Sandra Kubler.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Classification and Clustering Analysis using Weka Ishan Awadhesh
This Term Paper demonstrates the classification and clustering analysis on Bank Data using Weka. Classification Analysis is used to determine whether a particular customer would purchase a Personal Equity PLan or not while Clustering Analysis is used to analyze the behavior of various customer segments.
Pequeña introducción al laboratorio de Weka, para el curso BigData Analytics de la Universidad de los Andes, usando un ejemplo de clasificación de texto.
WEKA - A Data Mining Tool - by Shareek AhamedShareek Ahamed
WEKA project was initiated in early 1992 by the New Zealand Government, and more than 21 years have elapsed since the first public release of WEKA. Along that period of time, the software has been rewritten entirely from scratch once. Initially it was written in C programming language, and along the time it became a burden and they have moved to Java. These days, WEKA enjoys its acceptance in both academic and business levels because, WEKA is an open source project and it has an active community.
15 A/B Testing Stats That Will Blow your MindWishpond
A/B testing is a strategy in marketing in which two versions, A and B, (the Control and the Treatment) are tested against each other.
The goal is to identify changes that increase the chance of the what you want to occur, occurring.
Here are 15 awesome A/B testing statistics to inspire your own tests.
Week 4 Lecture 12 Significance Earlier we discussed co.docxcockekeshia
Week 4 Lecture 12
Significance
Earlier we discussed correlations without going into how we can identify statistically
significant values. Our approach to this uses the t-test. Unfortunately, Excel does not
automatically produce this form of the t-test, but setting it up within an Excel cell is fairly easy.
And, with some slight algebra, we can determine the minimum value that is statistically
significant for any table of correlations all of which have the same number of pairs (for example,
a Correlation table for our data set would use 50 pairs of values, since we have 50 members in
our sample).
The t-test formula for a correlation (r) is t = r * sqrt(n-2)/sqrt(1-r2); the associated degrees
of freedom are n-2 (number of pairs – 2) (Lind, Marchel, & Wathen, 2008). For some this might
look a bit off-putting, but remember that we can translate this into Excel cells and functions and
have Excel do the arithmetic for us.
Excel Example
If we go back to our correlation table for salary, midpoint, Age, Perf Rat, Service, and
Raise, we have:
Using Excel to create the formula and cell numbers for our key values allows us to
quickly create a result. The T.dist.2t gives us a p-value easily.
The formula to use in finding the minimum correlation value that is statistically
significant is r = sqrt(t^2/(t^2 + n-2)). We would find the appropriate t value by using the
t.inv.2T(alpha, df) with alpha = 0.05 and df = n-2 or 48. Plugging these values into the gives us
a t-value of 2.0106 or 2.011(rounded).
Putting 2.011 and 48 (n-2) into our formula gives us a r value of 0.278; therefore, in a
correlation table based on 50 pairs, any correlation greater or equal to 0.278 would be
statistically significant.
Technical Point. If you are interested in how we obtained the formula for determining
the minimum r value, the approach is shown below. If you are not interested in the math, you
can safely skip this paragraph.
t = r* sqrt(n-2)/sqrt(1-r2)
Multiplying gives us t *sqrt (1- r2) = r2* (n-2)
Squaring gives us: t2 * (1- r2) = r2* (n-2)
Multiplying out gives us: t2– t2* r2 = n r2-2* r2
Adding gives us: t2= n* r2-2*r2+ t2 *r2
Factoring gives us t2= r2 *(n -2+ t2)
Dividing gives us t2 / (n -2+ t2) = r2
Taking the square root gives us r = sqrt (t2 / (n -2+ t2)
Effect Size Measures
As we have discussed, there is a difference between statistical and practical
significance. Virtually any statistic can become statistically significant if the sample is large
enough. In practical terms, a correlation of .30 and below is generally considered too weak to be
of any practical significance. Additionally, the effect size measure for Pearson’s correlation is
simply the absolute value of the correlation; the outcome has the same general interpretation as
Cohen’s D for the t-test (0.8 is strong, and 0.2 is quite weak, for example) (Tanner & Youssef-
Morgan, 2013).
Spearman’s Rank Correlation
Another typ.
Machine Learning Unit-5 Decesion Trees & Random Forest.pdfAdityaSoraut
Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
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.
Data Science Interview Questions | Data Science Interview Questions And Answe...Simplilearn
This video on Data science interview questions will take you through some of the most popular questions that you face in your Data science interviews. It’s simply impossible to ignore the importance of data and our capacity to analyze, consolidate, and contextualize it. Data scientists are relied upon to fill this need, but there is a serious dearth of qualified candidates worldwide. If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you’ll need to show that you're technically proficient with Big Data concepts, frameworks, and applications. So, here we discuss the list of most popular questions you can expect in an interview and how to frame your answers.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Learn more at www.simplilearn.com/big-data-and-analytics/python-for-data-science-training
Similar to WEKA: Algorithms The Basic Methods (20)
As a business owner in Delaware, staying on top of your tax obligations is paramount, especially with the annual deadline for Delaware Franchise Tax looming on March 1. One such obligation is the annual Delaware Franchise Tax, which serves as a crucial requirement for maintaining your company’s legal standing within the state. While the prospect of handling tax matters may seem daunting, rest assured that the process can be straightforward with the right guidance. In this comprehensive guide, we’ll walk you through the steps of filing your Delaware Franchise Tax and provide insights to help you navigate the process effectively.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
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2. 1-rule Algorithm (1R) Way to find very easy classification rule Generates a one level decision tree which tests just one attribute Steps: Consider each attribute in turn There will be one branch in the decision tree for each value of this attribute Allot the majority class to each branch Repeat the same for all attributes and choose the one with minimum error
4. 1R in action Consider the problem of weather’s effect on play. Data is:
5. 1R in action Let us consider the Outlook parameter first Total Error = 4/14
6. 1R in action Consolidated table for all the attributes, ‘*’ represent arbitrary choice from equivalent options:
7. 1R in action From this table we can see that a decision tree on Outlook and Humidity gives minimum error We can choose and of these two attributes and the corresponding rules as our choice of classification rule Missing is treated as just another attribute, one branch in the decision tree dedicated to missing values like any other attribute value
8. Numeric attributes and 1R To deal with numeric attributes, we Discretize them The steps are : Sort instances on the basis of attribute’s value Place breakpoints where class changes These breakpoints gives us discrete numerical range Majority class of each range is considered as its range
10. Numeric attributes and 1R Applying the steps we get: The problem with this approach is that we can get a large number of division or Overfitting Therefore we enforce a minimum number of instances , for example taking min = 3 in above example, we get:
11. Numeric attributes and 1R When two adjacent division have the same majority class, then we can join these two divisions So after this we will get: Which gives the following classification rules:
12. Statistical Modeling Another classification technique Assumptions (for a given class): All attributes contributes equally to decision making All attributes are independent of each other
14. Statistical Modeling: An example Data Description: The upper half shows how many time a value of an attribute occurs for a class The lower half shows the same data in terms of fraction For example, class is yes 9 times For class = yes, outlook = sunny 2 times So under outlook = sunny and class = yes we have 2/9
15. Statistical Modeling Problem at hand: Solution: Taking into the consideration that all attributes equally and are independent Likelihood of yes = 2/9x3/9x3/9x3/9x9/14 = 0.0053 Likelihood of no = 3/5x1/5x4/5x3/5x5/14 = 0.0206
16. Statistical Modeling: An example Solution continued.. As can be observed, likelihood of yes is high Using normalization, we can calculate probability as: Probability of yes = (.0053)/(.0053 + .0206) = 20.5% Probability of no = (.0206)/(.0053 + .0206) = 79.5%
17. Statistical Modeling: An example Derivation using Bayes’ rule: Acc to Bayes’ rule, for a hypothesis H and evidence E that bears on that hypothesis, then P[H|E] = (P[E|H] x P[H]) / P[E] For our example hypothesis H is that play will be, say, yes and E is the particular combination of attribute values at hand Outlook = sunny(E1) Temperature = cool (E2) Humidity = high(E3) Windy = True (E4)
18. Statistical Modeling: An example Derivation using Bayes’ rule: Now since E1, E2, E3 and E4 are independent therefore we have P[H|E] = (P[E1|H] x P[E2|H] x P[E3|H] x P[E4|H] x P[H] ) / P[E] Replacing values from the table we get, P[yes|E] = (2/9 x 3/9 x 3/9 x 3/9 x 9/14) / P[E] P[E] will be taken care of during normalization of P[yes|E] and P[No|E] This method is called as Naïve Bayes
19. Problem and Solution for Naïve Bayes Problem: In case we have an attribute value (Ea)for which P[Ea|H] = 0, then irrespective of other attributes P[H|E] = 0 Solution: We can add a constant to numerator and denominator, a technique called Laplace Estimator for example, P1 + P2 + P3 = 1:
20. Statistical Modeling: Dealing with missing attributes Incase an value is missing, say for attribute Ea in the given data set, we just don’t count it while calculating the P[Ea|H] Incase an attribute is missing in the instance to be classified, then its factor is not there in the expression for P[H|E], for example if outlook is missing then we will have: Likelihood of Yes = 3/9 x 3/9 x 3/9 x 9/14 = 0.0238 Likelihood of No = 1/5 x 4/5 x 3/5 x 5/14 = 0.0343
21. Statistical Modeling: Dealing with numerical attributes Numeric values are handled by assuming that they have : Normal probability distribution Gaussian probability distribution For a normal distribution we have: u = mean sigma = Standard deviation x = instance under consideration f(x) = contribution of to likelihood figures
23. Statistical Modeling: Dealing with numerical attributes So here we have calculated the mean and standard deviation for numerical attributes like temperature and humidity For temperature = 66 So the contribution of temperature = 66 in P[yes|E] is 0.0340 We do this similarly for other numerical attributes
24. Divide-and-Conquer: Constructing Decision Trees Steps to construct a decision tree recursively: Select an attribute to placed at root node and make one branch for each possible value Repeat the process recursively at each branch, using only those instances that reach the branch If at any time all instances at a node have the classification, stop developing that part of the tree Problem: How to decide which attribute to split on
25. Divide-and-Conquer: Constructing Decision Trees Steps to find the attribute to split on: We consider all the possible attributes as option and branch them according to different possible values Now for each possible attribute value we calculate Information and then find the Information gain for each attribute option Select that attribute for division which gives a Maximum Information Gain Do this until each branch terminates at an attribute which gives Information = 0
26. Divide-and-Conquer: Constructing Decision Trees Calculation of Information and Gain: For data: (P1, P2, P3……Pn) such that P1 + P2 + P3 +……. +Pn = 1 Information(P1, P2 …..Pn) = -P1logP1 -P2logP2 – P3logP3 ……… -PnlogPn Gain= Information before division – Information after division
27. Divide-and-Conquer: Constructing Decision Trees Example: Here we have consider each attribute individually Each is divided into branches according to different possible values Below each branch the number of class is marked
28. Divide-and-Conquer: Constructing Decision Trees Calculations: Using the formulae for Information, initially we have Number of instances with class = Yes is 9 Number of instances with class = No is 5 So we have P1 = 9/14 and P2 = 5/14 Info[9/14, 5/14] = -9/14log(9/14) -5/14log(5/14) = 0.940 bits Now for example lets consider Outlook attribute, we observe the following:
29. Divide-and-Conquer: Constructing Decision Trees Example Contd. Gain by using Outlook for division = info([9,5]) – info([2,3],[4,0],[3,2]) = 0.940 – 0.693 = 0.247 bits Gain (outlook) = 0.247 bits Gain (temperature) = 0.029 bits Gain (humidity) = 0.152 bits Gain (windy) = 0.048 bits So since Outlook gives maximum gain, we will use it for division And we repeat the steps for Outlook = Sunny and Rainy and stop for Overcast since we have Information = 0 for it
30. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: The problem If we follow the previously subscribed method, it will always favor an attribute with the largest number of branches In extreme cases it will favor an attribute which has different value for each instance: Identification code
31. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: The problem Information for such an attribute is 0 info([0,1]) + info([0,1]) + info([0,1]) + …………. + info([0,1]) = 0 It will hence have the maximum gain and will be chosen for branching But such an attribute is not good for predicting class of an unknown instance nor does it tells anything about the structure of division So we use gain ratio to compensate for this
32. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: Gain ratio Gain ratio = gain/split info To calculate split info, for each instance value we just consider the number of instances covered by each attribute value, irrespective of the class Then we calculate the split info, so for identification code with 14 different values we have: info([1,1,1,…..,1]) = -1/14 x log1/14 x 14 = 3.807 For Outlook we will have the split info: info([5,4,5]) = -1/5 x log 1/5 -1/4 x log1/4 -1/5 x log 1/5 = 1.577
34. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: Gain ratio Though the ‘highly branched attribute’ still have the maximum gain ratio, but its advantage is greatly reduced Problem with using gain ratio: In some situations the gain ratio modification overcompensates and can lead to preferring an attribute just because its intrinsic information is much lower than that for the other attributes. A standard fix is to choose the attribute that maximizes the gain ratio, provided that the information gain or that attribute is at least as great as the average information gain for all the attributes examined
35. Covering Algorithms: Constructing rules Approach: Consider each class in turn Seek a way of covering all instances in it, excluding instances not belonging to this class Identify a rule to do so This is called a covering approach because at each stage we identify a rule that covers some of the instances
38. If x > 1.2 and y > 2.6 then class = a If x > 1.4 and y < 2.4 then class = a
39. Covering Algorithms: Constructing rules Rules Vs Trees: Covering algorithm covers only a single class at a time whereas division takes all the classes in account as decision trees creates a combines concept description Problem of replicated sub trees is avoided in rules Tree for the previous problem:
41. Covering Algorithms: Constructing rules PRISM Algorithm: Criteria to select an attribute for division Include as many instances of the desired class and exclude as many instances of other class as possible If a new rule covers t instances of which p are positive examples of the class and t-p are instances of other classes i.e errors, then try to maximize p/t
43. Covering Algorithms: Constructing rules PRISM Algorithm: In action We start with the class = hard and have the following rule: If ? Then recommendation = hard Here ? represents an unknown rule For unknown we have nine choices:
44. Covering Algorithms: Constructing rules PRISM Algorithm: In action Here the maximum t/p ratio is for astigmatism = yes (choosing randomly between equivalent option in case there coverage is also same) So we get the rule: If astigmatism = yes then recommendation = hard We wont stop at this rule as this rule gives only 4 correct results out of 12 instances it covers We remove the correct instances of the above rule from our example set and start with the rule: If astigmatism = yes and ? then recommendation = hard
46. Covering Algorithms: Constructing rules PRISM Algorithm: In action And the choices for this data is: We choose tear production rate = normal which has highest t/p
47. Covering Algorithms: Constructing rules PRISM Algorithm: In action So we have the rule: If astigmatism = yes and tear production rate = normal then recommendation = hard Again, we remove matched instances, now we have the data:
48. Covering Algorithms: Constructing rules PRISM Algorithm: In action Now again using t/p we finally have the rule (based on maximum coverage): If astigmatism = yes and tear production rate = normal and spectacle prescription = myope then recommendation = hard And so on. …..
50. Covering Algorithms: Constructing rules Rules Vs decision lists The rules produced, for example by PRISM algorithm, are not necessarily to be interpreted in order like decision lists There is no order in which class should be considered while generating rules Using rules for classification, one instance may receive multiple receive multiple classification or no classification at all In such cases go for the rule with maximum coverage and training examples respecitively These difficulties are not there with decision lists as they are to be interpreted in order and have a default rule at the end
51. Mining Association Rules Definition: An association rule can predict any number of attributes and also any combination of attributes Parameter for selecting an Association Rule: Coverage: The number of instances they predict correctly Accuracy: The ratio of coverageand total number of instances the rule is applicable We want association rule with high coverage and a minimum specified accuracy
52. Mining Association Rules Terminology: Item – set: A combination of attributes Item: An attribute – value pair An example: For the weather data we have a table with each column containing an item – set having different number of attributes With each entry the coverage is also given The table is not complete, just gives us a good idea
54. Mining Association Rules Generating Association rules: We need to specify a minimum coverage and accuracy for the rules to be generated before hand Steps: Generate the item sets Each item set can be permuted to generate a number of rules For each rule check if the coverage and accuracy is appropriate This is how we generate association rules
55. Mining Association Rules Generating Association rules: For example if we take the item set: humidity = normal, windy = false, play = yes This gives seven potential rules (with accuracy):
56. Linear models We will look at methods to deal with the prediction of numerical quantities We will see how to use numerical methods for classification
57. Linear models Numerical Prediction: Linear regression Linear regression is a technique to predict numerical quantities Here we express the class (a numerical quantity) as a linear combination of attributes with predetermined weights For example if we have attributes a1,a2,a3…….,ak x = (w0) + (w1)x(a1) + (w2)x(a2) + …… + (wk)x(ak) Here x represents the predicted class and w0,w1……,wk are the predetermined weights
58. Linear models Numerical Prediction: Linear regression The weights are calculated by using the training set To choose optimum weights we select the weights with minimum square sum:
59. Linear models Linear classification: Multi response linear regression For each class we use linear regression to get a linear expression When the instance belongs to the class output is 1, otherwise 0 Now for an unclassified instance we use the expression for each class and get an output The class expression giving the maximum output is selected as the classified class This method has the drawbacks that values produced are not proper probabilities
60. Linear models Linear classification: Logistic regression To get the output as proper probabilities in the range 0 to 1 we use logistic regression Here the output y is defined as: y = 1/(1+e^(-x)) x = (w0) + (w1)x(a1) + (w2)x(a2) + …… + (wk)x(ak) So the output y will lie in the range (0,1]
61. Linear models Linear classification: Logistic regression To select appropriate weights for the expression of x, we maximize: To generalize Logistic regression we can use do the calculation like we did in Multi response linear regression Again the problem with this approach is that the probabilities of different classes do not sum up to 1
62. Linear models Linear classification using the perceptron If instances belonging to different classes can be divided in the instance space by using hyper planes, then they are called linearly separable If instances are linearly separable then we can use perceptron learning rule for classification Steps: Lets assume that we have only 2 classes The equation of hyper plane is (a0 = 1): (w0)(a0) + (w1)(a1) + (w2)(a2) +…….. + (wk)(ak) = 0
63. Linear models Linear classification using the perceptron Steps (contd.): If the sum (mentioned in previous step) is greater than 0 than we have first class else the second one The algorithm to get the weight and hence the equation of dividing hyper plane (or the perceptron)is:
64. Instance-based learning General steps: No preprocessing of training sets, just store the training instances as it is To classify a new instance calculate its distance with every stored training instance The unclassified instance is allotted the class of the instance which has the minimum distance from it
65. Instance-based learning The distance function The distance function we use depends on our application Some of the popular distance functions are: Euclidian distance, Manhattan distance metric etc. The most popular distance metric is Euclidian distance (between teo instances) given by: K is the number of attributes
66. Instance-based learning Normalization of data: We normalize attributes such that they lie in the range [0,1], by using the formulae: Missing attributes: In case of nominal attributes, if any of the two attributes are missing or if the attributes are different, the distance is taken as 1 In nominal attributes, if both are missing than difference is 1. If only one attribute is missing than the difference is the either the normalized value of given attribute or one minus that size, which ever is bigger
67. Instance-based learning Finding nearest neighbors efficiently: Finding nearest neighbor by calculating distance with every attribute of each instance if linear We make this faster by using kd-trees KD-Trees: They are binary trees that divide the input space with a hyper plane and then split each partition again, recursively It stores the points in k dimensional space, k being the number of attributes
69. Instance-based learning Finding nearest neighbors efficiently: Here we see a kd tree and the instances and splits with k=2 As you can see not all child nodes are developed to the same depth We have mentioned the axis along which the division has been done (v or h in this case) Steps to find the nearest neighbor: Construct the kd tree (explained later) Now start from the root node and comparing the appropriate attribute (based on the axis along which the division has been done), move to left or the right sub-tree
70. Instance-based learning Steps to find the nearest neighbor (contd.): Repeat this step recursively till you reach a node which is either a leaf node or has no appropriate leaf node (left or right) Now you have find the region to which this new instance belong You also have a probable nearest neighbor in the form of the regions leaf node (or immediate neighbor) Calculate the distance of the instance with the probable nearest neighbor. Any closer instance will lie in a circle with radius equal to this distance
71. Instance-based learning Finding nearest neighbors efficiently: Steps to find the nearest neighbor (contd.): Now we will move redo our recursive trace looking for an instance which is closer to put unclassified instance than the probable nearest neighbor we have We start with the immediate neighbor, if it lies in the circle than we will have to consider it and all its child nodes (if any) If condition of previous step is not true then we check the siblings of the parent of our probable nearest neighbor We repeat these steps till we reach the root In case we find instance(s) which are nearer, we update the nearest neighbor
73. Instance-based learning Construction of KD tree: We need to figure out two things to construct a kd tree: Along which dimension to make the cut Which instance to use to make the cut Deciding the dimension to make the cut: We calculate the variance along each axis The division is done perpendicular to the axis with minimum variance Deciding the instance to be used for division: Just take the median as the point of division So we repeat these steps recursively till all the points are exhausted
74. Clustering Clustering techniques apply when rather than predicting the class, we just want the instances to be divided into natural group Iterative instance based learning: k-means Here k represents the number of clusters The instance space is divided in to k clusters K-means forms the cluster so as the sum of square distances of instances from there cluster center is minimum
75. Clustering Steps: Decide the number of clusters or k manually Now from the instance set to be clustered, randomly select k points. These will be our initial k cluster centers of our k clusters Now take each instance one by one , calculate its distance from all the cluster centers and allot it to the cluster for which it has the minimum distance Once all the instances have been classified, take centroid of all the points in a cluster. This centroid will be give the new cluster center Again re-cluster all the instances followed by taking the centroid to get yet another cluster center Repeat step 5 till we reach the stage in which the cluster centers don’t change. Stop at this, we have our k-clusters
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