The document discusses machine learning and various machine learning concepts. It defines learning as improving performance through experience. Machine learning involves using data to acquire models and learn hidden concepts. The main areas covered are supervised learning (data with labels), unsupervised learning (data without labels), semi-supervised learning (some labels present), and reinforcement learning (agent takes actions and receives rewards/punishments). Decision trees are presented as a way to represent hypotheses learned through examples, with attributes used to recursively split data into partitions.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. It describes supervised learning where data and labels are provided, unsupervised learning where only data is given, and reinforcement learning where an agent takes actions and receives rewards or punishments. Decision tree learning is discussed as a supervised learning method where trees are constructed by recursively splitting data based on attribute tests that optimize criteria like information gain. Overfitting and techniques like pruning are addressed to improve generalization.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. The document outlines different types of machine learning including supervised learning (using labeled data), unsupervised learning (using only unlabeled data), and reinforcement learning (where an agent takes actions and receives rewards or punishments). It provides examples of classification problems and discusses decision tree learning as a supervised learning method, including how decision trees are constructed and potential issues like overfitting.
The document discusses machine learning concepts including supervised and unsupervised learning. It explains that supervised learning involves labeled training data to learn a model that can classify new examples, while unsupervised learning discovers hidden patterns in unlabeled data. The document also covers regression, classification tasks, evaluating models on test data, feature selection, and the machine learning process of data collection, model training and evaluation.
Chapter 4 Classification in data sience .pdfAschalewAyele2
This document discusses data mining tasks related to predictive modeling and classification. It defines predictive modeling as using historical data to predict unknown future values, with a focus on accuracy. Classification is described as predicting categorical class labels based on a training set. Several classification algorithms are mentioned, including K-nearest neighbors, decision trees, neural networks, Bayesian networks, and support vector machines. The document also discusses evaluating classification performance using metrics like accuracy, precision, recall, and a confusion matrix.
This document provides an introduction and overview of machine learning. It discusses different types of machine learning including supervised, unsupervised, semi-supervised and reinforcement learning. It also covers key machine learning concepts like hypothesis space, inductive bias, representations, features, and more. The document provides examples to illustrate these concepts in domains like medical diagnosis, entity recognition, and image recognition.
An introduction to machine learning and statisticsSpotle.ai
This document provides an overview of machine learning and predictive modeling. It begins by describing how predictive models can be used in various domains like healthcare, finance, telecom, and business. It then discusses the differences between machine learning and predictive modeling, noting that machine learning aims to allow machines to learn autonomously using feedback mechanisms, while predictive modeling focuses on building statistical models to predict outcomes. The document also uses examples like Microsoft's Tay chatbot to illustrate how machine learning systems can be exposed to real-world data to continuously learn and improve. It concludes by explaining how predictive analytics fits within machine learning as the starting point to build initial predictive models and continuously monitor and refine them.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. It describes supervised learning where data and labels are provided, unsupervised learning where only data is given, and reinforcement learning where an agent takes actions and receives rewards or punishments. Decision tree learning is discussed as a supervised learning method where trees are constructed by recursively splitting data based on attribute tests that optimize criteria like information gain. Overfitting and techniques like pruning are addressed to improve generalization.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. The document outlines different types of machine learning including supervised learning (using labeled data), unsupervised learning (using only unlabeled data), and reinforcement learning (where an agent takes actions and receives rewards or punishments). It provides examples of classification problems and discusses decision tree learning as a supervised learning method, including how decision trees are constructed and potential issues like overfitting.
The document discusses machine learning concepts including supervised and unsupervised learning. It explains that supervised learning involves labeled training data to learn a model that can classify new examples, while unsupervised learning discovers hidden patterns in unlabeled data. The document also covers regression, classification tasks, evaluating models on test data, feature selection, and the machine learning process of data collection, model training and evaluation.
Chapter 4 Classification in data sience .pdfAschalewAyele2
This document discusses data mining tasks related to predictive modeling and classification. It defines predictive modeling as using historical data to predict unknown future values, with a focus on accuracy. Classification is described as predicting categorical class labels based on a training set. Several classification algorithms are mentioned, including K-nearest neighbors, decision trees, neural networks, Bayesian networks, and support vector machines. The document also discusses evaluating classification performance using metrics like accuracy, precision, recall, and a confusion matrix.
This document provides an introduction and overview of machine learning. It discusses different types of machine learning including supervised, unsupervised, semi-supervised and reinforcement learning. It also covers key machine learning concepts like hypothesis space, inductive bias, representations, features, and more. The document provides examples to illustrate these concepts in domains like medical diagnosis, entity recognition, and image recognition.
An introduction to machine learning and statisticsSpotle.ai
This document provides an overview of machine learning and predictive modeling. It begins by describing how predictive models can be used in various domains like healthcare, finance, telecom, and business. It then discusses the differences between machine learning and predictive modeling, noting that machine learning aims to allow machines to learn autonomously using feedback mechanisms, while predictive modeling focuses on building statistical models to predict outcomes. The document also uses examples like Microsoft's Tay chatbot to illustrate how machine learning systems can be exposed to real-world data to continuously learn and improve. It concludes by explaining how predictive analytics fits within machine learning as the starting point to build initial predictive models and continuously monitor and refine them.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
NN Classififcation Neural Network NN.pptxcmpt cmpt
The document provides an overview of classification methods. It begins with defining classification tasks and discussing evaluation metrics like accuracy, recall, and precision. It then describes several common classification techniques including nearest neighbor classification, decision tree induction, Naive Bayes, logistic regression, and support vector machines. For decision trees specifically, it explains the basic structure of decision trees, the induction process using Hunt's algorithm, and issues in tree induction.
The document describes decision tree induction and classification. It provides examples of datasets that can be used to build decision trees, including examples on loan approvals, playing sailboats, and shapes. It explains the basic TDIDT (Top Down Induction of Decision Trees) algorithm for building decision trees from datasets in a recursive partitioning manner. Key aspects of decision tree learning covered include attribute selection criteria, information gain, entropy, and residual information.
The document discusses various topics related to active learning and optimal experimental design. It begins with motivations for active learning such as collecting higher quality data being more useful than simply more data, and data collection often being expensive. It provides examples of applying active learning techniques to problems like classification with gene expression data, collaborative filtering for movie recommendations, sequencing genomes, and improving cell culture conditions. It then covers topics like uncertainty sampling, query by committee, and information-based loss functions for active learning. For optimal experimental design, it discusses techniques like A-optimal, D-optimal, and E-optimal design and how they can be applied to problems with linear models. It also covers extensions to non-linear models using techniques like sequential experimental design
This document discusses various methods for evaluating machine learning models. It describes splitting data into training, validation, and test sets to evaluate models on large datasets. For small or unbalanced datasets, it recommends cross-validation techniques like k-fold cross-validation and stratified sampling. The document also covers evaluating classifier performance using metrics like accuracy, confidence intervals, and lift charts, as well as addressing issues that can impact evaluation like overfitting and class imbalance.
This document discusses machine learning and artificial intelligence concepts such as supervised learning, decision trees, and neural networks. It defines machine learning, describes categories of learning including learning from examples and reinforcement learning, and explains algorithms like naive Bayes classifiers and decision tree induction. Performance measurement and challenges for different machine learning approaches are also summarized.
This document discusses machine learning and artificial intelligence concepts such as supervised learning, decision trees, and neural networks. It defines machine learning, describes categories of learning including learning from examples and reinforcement learning, and explains algorithms like naive Bayes classifiers and decision tree induction. Performance measurement and challenges for different machine learning approaches are also summarized.
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
Top 10 Data Science Practioner Pitfalls - Mark LandrySri Ambati
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, we review top 10 common pitfalls and steps to avoid them. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Statistical Learning and Model Selection (1).pptxrajalakshmi5921
This document discusses statistical learning and model selection. It introduces statistical learning problems, statistical models, the need for statistical modeling, and issues around evaluating models. Key points include: statistical learning involves using data to build a predictive model; a good model balances bias and variance to minimize prediction error; cross-validation is described as the ideal procedure for evaluating models without overfitting to the test data.
Top 10 Data Science Practitioner PitfallsSri Ambati
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, Mark Landry, one of the world’s leading Kagglers, will review the top 10 common pitfalls and steps to avoid them.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses techniques for evaluating and improving classifiers. It begins by explaining how to evaluate a classifier's accuracy using metrics like accuracy, precision, recall, and F-measure. It introduces the confusion matrix and shows how different parts of the matrix relate to these metrics. The document then discusses issues like overfitting, underfitting, bias and variance that can impact a classifier's performance. It explains that the goal is to balance bias and variance to minimize total error and achieve optimal classification.
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Top 10 Data Science Practitioner PitfallsSri Ambati
Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Lecture 09(introduction to machine learning)Jeet Das
Machine learning allows computers to learn without explicit programming by analyzing data to recognize patterns and make predictions. It can be supervised, learning from labeled examples to classify new data, or unsupervised, discovering hidden patterns in unlabeled data through clustering. Key aspects include feature representation, distance metrics to compare examples, and evaluation methods like measuring error on test data to avoid overfitting to the training data.
Machine learning is the process of using algorithms to analyze data and learn from it. The document discusses several machine learning concepts including supervised and unsupervised learning, decision trees, and inductive learning. It provides examples of using decision trees to classify restaurant customers as waiting or leaving based on attributes like reservation status and restaurant fullness. Key algorithms like ID3 use information gain to build decision trees from training data in a top-down greedy manner.
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering, and the inductive learning problem. Key algorithms like ID3 for learning decision trees are described. Information theory concepts such as entropy, information gain, and the Huffman coding algorithm are also covered as they relate to evaluating and selecting attributes for decision tree learning.
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering. It also covers the inductive learning problem, decision tree learning with the ID3 algorithm, and uses information theory concepts like entropy and information gain to select the best attributes to split data. An example decision tree is constructed for a restaurant domain to classify whether a patron will wait or leave.
Classification and prediction models are used to categorize data or predict unknown values. Classification predicts categorical class labels to classify new data based on attributes in a training set, while prediction models continuous values. Common applications include credit approval, marketing, medical diagnosis, and treatment analysis. The classification process involves building a model from a training set and then using the model to classify new data, estimating accuracy on a test set.
NN Classififcation Neural Network NN.pptxcmpt cmpt
The document provides an overview of classification methods. It begins with defining classification tasks and discussing evaluation metrics like accuracy, recall, and precision. It then describes several common classification techniques including nearest neighbor classification, decision tree induction, Naive Bayes, logistic regression, and support vector machines. For decision trees specifically, it explains the basic structure of decision trees, the induction process using Hunt's algorithm, and issues in tree induction.
The document describes decision tree induction and classification. It provides examples of datasets that can be used to build decision trees, including examples on loan approvals, playing sailboats, and shapes. It explains the basic TDIDT (Top Down Induction of Decision Trees) algorithm for building decision trees from datasets in a recursive partitioning manner. Key aspects of decision tree learning covered include attribute selection criteria, information gain, entropy, and residual information.
The document discusses various topics related to active learning and optimal experimental design. It begins with motivations for active learning such as collecting higher quality data being more useful than simply more data, and data collection often being expensive. It provides examples of applying active learning techniques to problems like classification with gene expression data, collaborative filtering for movie recommendations, sequencing genomes, and improving cell culture conditions. It then covers topics like uncertainty sampling, query by committee, and information-based loss functions for active learning. For optimal experimental design, it discusses techniques like A-optimal, D-optimal, and E-optimal design and how they can be applied to problems with linear models. It also covers extensions to non-linear models using techniques like sequential experimental design
This document discusses various methods for evaluating machine learning models. It describes splitting data into training, validation, and test sets to evaluate models on large datasets. For small or unbalanced datasets, it recommends cross-validation techniques like k-fold cross-validation and stratified sampling. The document also covers evaluating classifier performance using metrics like accuracy, confidence intervals, and lift charts, as well as addressing issues that can impact evaluation like overfitting and class imbalance.
This document discusses machine learning and artificial intelligence concepts such as supervised learning, decision trees, and neural networks. It defines machine learning, describes categories of learning including learning from examples and reinforcement learning, and explains algorithms like naive Bayes classifiers and decision tree induction. Performance measurement and challenges for different machine learning approaches are also summarized.
This document discusses machine learning and artificial intelligence concepts such as supervised learning, decision trees, and neural networks. It defines machine learning, describes categories of learning including learning from examples and reinforcement learning, and explains algorithms like naive Bayes classifiers and decision tree induction. Performance measurement and challenges for different machine learning approaches are also summarized.
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
Top 10 Data Science Practioner Pitfalls - Mark LandrySri Ambati
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, we review top 10 common pitfalls and steps to avoid them. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Statistical Learning and Model Selection (1).pptxrajalakshmi5921
This document discusses statistical learning and model selection. It introduces statistical learning problems, statistical models, the need for statistical modeling, and issues around evaluating models. Key points include: statistical learning involves using data to build a predictive model; a good model balances bias and variance to minimize prediction error; cross-validation is described as the ideal procedure for evaluating models without overfitting to the test data.
Top 10 Data Science Practitioner PitfallsSri Ambati
Over-fitting, misread data, NAs, collinear column elimination and other common issues play havoc in the day of practicing data scientist. In this talk, Mark Landry, one of the world’s leading Kagglers, will review the top 10 common pitfalls and steps to avoid them.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses techniques for evaluating and improving classifiers. It begins by explaining how to evaluate a classifier's accuracy using metrics like accuracy, precision, recall, and F-measure. It introduces the confusion matrix and shows how different parts of the matrix relate to these metrics. The document then discusses issues like overfitting, underfitting, bias and variance that can impact a classifier's performance. It explains that the goal is to balance bias and variance to minimize total error and achieve optimal classification.
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Top 10 Data Science Practitioner PitfallsSri Ambati
Top 10 Data Science Practitioner Pitfalls Meetup with Erin LeDell and Mark Landry on 09.09.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Lecture 09(introduction to machine learning)Jeet Das
Machine learning allows computers to learn without explicit programming by analyzing data to recognize patterns and make predictions. It can be supervised, learning from labeled examples to classify new data, or unsupervised, discovering hidden patterns in unlabeled data through clustering. Key aspects include feature representation, distance metrics to compare examples, and evaluation methods like measuring error on test data to avoid overfitting to the training data.
Machine learning is the process of using algorithms to analyze data and learn from it. The document discusses several machine learning concepts including supervised and unsupervised learning, decision trees, and inductive learning. It provides examples of using decision trees to classify restaurant customers as waiting or leaving based on attributes like reservation status and restaurant fullness. Key algorithms like ID3 use information gain to build decision trees from training data in a top-down greedy manner.
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering, and the inductive learning problem. Key algorithms like ID3 for learning decision trees are described. Information theory concepts such as entropy, information gain, and the Huffman coding algorithm are also covered as they relate to evaluating and selecting attributes for decision tree learning.
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering. It also covers the inductive learning problem, decision tree learning with the ID3 algorithm, and uses information theory concepts like entropy and information gain to select the best attributes to split data. An example decision tree is constructed for a restaurant domain to classify whether a patron will wait or leave.
Classification and prediction models are used to categorize data or predict unknown values. Classification predicts categorical class labels to classify new data based on attributes in a training set, while prediction models continuous values. Common applications include credit approval, marketing, medical diagnosis, and treatment analysis. The classification process involves building a model from a training set and then using the model to classify new data, estimating accuracy on a test set.
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
In a tight labour market, job-seekers gain bargaining power and leverage it into greater job quality—at least, that’s the conventional wisdom.
Michael, LMIC Economist, presented findings that reveal a weakened relationship between labour market tightness and job quality indicators following the pandemic. Labour market tightness coincided with growth in real wages for only a portion of workers: those in low-wage jobs requiring little education. Several factors—including labour market composition, worker and employer behaviour, and labour market practices—have contributed to the absence of worker benefits. These will be investigated further in future work.
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
How Does CRISIL Evaluate Lenders in India for Credit RatingsShaheen Kumar
CRISIL evaluates lenders in India by analyzing financial performance, loan portfolio quality, risk management practices, capital adequacy, market position, and adherence to regulatory requirements. This comprehensive assessment ensures a thorough evaluation of creditworthiness and financial strength. Each criterion is meticulously examined to provide credible and reliable ratings.
5 Tips for Creating Standard Financial ReportsEasyReports
Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Vicinity Jobs’ data includes more than three million 2023 OJPs and thousands of skills. Most skills appear in less than 0.02% of job postings, so most postings rely on a small subset of commonly used terms, like teamwork.
Laura Adkins-Hackett, Economist, LMIC, and Sukriti Trehan, Data Scientist, LMIC, presented their research exploring trends in the skills listed in OJPs to develop a deeper understanding of in-demand skills. This research project uses pointwise mutual information and other methods to extract more information about common skills from the relationships between skills, occupations and regions.
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
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2. 2
2
What is Learning?
• Herbert Simon: “Learning is any process
by which a system improves performance
from experience.”
• “A computer program is said to learn from
experience E with respect to some class of
tasks T and performance measure P, if its
performance at tasks in T, as measured by
P, improves with experience E.”
– Tom Mitchell
3. 3
Learning
• Learning is essential for unknown environments,
– i.e., when designer lacks omniscience
• Learning is useful as a system construction
method,
– i.e., expose the agent to reality rather than trying to
write it down
• Learning modifies the agent's decision
mechanisms to improve performance
4. 4
Machine Learning
• Machine learning: how to acquire a model
on the basis of data / experience
– Learning parameters (e.g. probabilities)
– Learning structure (e.g. BN graphs)
– Learning hidden concepts (e.g. clustering)
5. 5
Machine Learning Areas
• Supervised Learning: Data and corresponding labels
are given
• Unsupervised Learning: Only data is given, no labels
provided
• Semi-supervised Learning: Some (if not all) labels are
present
• Reinforcement Learning: An agent interacting with the
world makes observations, takes actions, and is
rewarded or punished; it should learn to choose actions
in such a way as to obtain a lot of reward
6. 6
Supervised Learning : Important Concepts
• Data: labeled instances <xi, y>, e.g. emails marked
spam/not spam
– Training Set
– Held-out Set
– Test Set
• Features: attribute-value pairs which characterize each x
• Experimentation cycle
– Learn parameters (e.g. model probabilities) on training set
– (Tune hyper-parameters on held-out set)
– Compute accuracy of test set
– Very important: never “peek” at the test set!
• Evaluation
– Accuracy: fraction of instances predicted correctly
• Overfitting and generalization
– Want a classifier which does well on test data
– Overfitting: fitting the training data very closely, but not
generalizing well
9. 9
Classification Examples
• In classification, we predict labels y (classes) for inputs x
• Examples:
– OCR (input: images, classes: characters)
– Medical diagnosis (input: symptoms, classes: diseases)
– Automatic essay grader (input: document, classes: grades)
– Fraud detection (input: account activity, classes: fraud / no fraud)
– Customer service email routing
– Recommended articles in a newspaper, recommended books
– DNA and protein sequence identification
– Categorization and identification of astronomical images
– Financial investments
– … many more
10. 10
Inductive learning
• Simplest form: learn a function from examples
•
• f is the target function
• An example is a pair (x, f(x))
•
• Pure induction task:
– Given a collection of examples of f, return a
function h that approximates f.
– find a hypothesis h, such that h ≈ f, given a training
set of examples
–
• (This is a highly simplified model of real learning:
11. 11
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
•
12. 12
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
•
13. 13
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
•
14. 14
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
•
15. 15
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
16. 16
Inductive learning method
• Construct/adjust h to agree with f on training set
• (h is consistent if it agrees with f on all examples)
•
• E.g., curve fitting:
• Ockham’s razor: prefer the simplest hypothesis
consistent with data
17. 17
17
Generalization
• Hypotheses must generalize to correctly
classify instances not in the training data.
• Simply memorizing training examples is a
consistent hypothesis that does not
generalize.
• Occam’s razor:
– Finding a simple hypothesis helps ensure
generalization.
19. 19
Supervised Learning
• Learning a discrete function: Classification
– Boolean classification:
• Each example is classified as true(positive) or
false(negative).
• Learning a continuous function: Regression
20. 20
Data Mining: Concepts and Techniques 20
Classification—A Two-Step Process
• Model construction: describing a set of predetermined classes
– Each tuple/sample is assumed to belong to a predefined class,
as determined by the class label
– The set of tuples used for model construction is training set
– The model is represented as classification rules, decision
trees, or mathematical formulae
• Model usage: for classifying future or unknown objects
– Estimate accuracy of the model
• The known label of test sample is compared with the
classified result from the model
• Test set is independent of training set, otherwise over-
fitting will occur
– If the accuracy is acceptable, use the model to classify data
tuples whose class labels are not known
21. 21
Illustrating Classification Task
Apply
Model
Induction
Deduction
Learn
Model
Model
Tid Attrib1 Attrib2 Attrib3 Class
1 Yes Large 125K No
2 No Medium 100K No
3 No Small 70K No
4 Yes Medium 120K No
5 No Large 95K Yes
6 No Medium 60K No
7 Yes Large 220K No
8 No Small 85K Yes
9 No Medium 75K No
10 No Small 90K Yes
10
Tid Attrib1 Attrib2 Attrib3 Class
11 No Small 55K ?
12 Yes Medium 80K ?
13 Yes Large 110K ?
14 No Small 95K ?
15 No Large 67K ?
10
Test Set
Learning
algorithm
Training Set
22. 22
Data Mining: Concepts and Techniques
Issues: Data Preparation
• Data cleaning
– Preprocess data in order to reduce noise and
handle missing values
• Relevance analysis (feature selection)
– Remove the irrelevant or redundant attributes
• Data transformation
– Generalize data to (higher concepts, discretization)
– Normalize attribute values
23. 23
Classification Techniques
• Decision Tree based Methods
• Rule-based Methods
• Naïve Bayes and Bayesian Belief
Networks
• Neural Networks
• Support Vector Machines
• and more...
24. 24
Learning decision trees
Example Problem: decide whether to wait for a table at a
restaurant, based on the following attributes:
1. Alternate: is there an alternative restaurant nearby?
2. Bar: is there a comfortable bar area to wait in?
3. Fri/Sat: is today Friday or Saturday?
4. Hungry: are we hungry?
5. Patrons: number of people in the restaurant (None, Some, Full)
6. Price: price range ($, $$, $$$)
7. Raining: is it raining outside?
8. Reservation: have we made a reservation?
9. Type: kind of restaurant (French, Italian, Thai, Burger)
10. WaitEstimate: estimated waiting time (0-10, 10-30, 30-60, >60)
25. 25
Feature(Attribute)-based representations
• Examples described by feature(attribute) values
– (Boolean, discrete, continuous)
• E.g., situations where I will/won't wait for a table:
• Classification of examples is positive (T) or negative (F)
•
26. 26
Decision trees
• One possible representation for hypotheses
• E.g., here is the “true” tree for deciding whether to wait:
27. 27
Expressiveness
• Decision trees can express any function of the input attributes.
• E.g., for Boolean functions, truth table row → path to leaf:
• Trivially, there is a consistent decision tree for any training set with
one path to leaf for each example (unless f nondeterministic in x) but
it probably won't generalize to new examples
• Prefer to find more compact decision trees
28. 28
Decision tree learning
• Aim: find a small tree consistent with the training examples
• Idea: (recursively) choose "most significant" attribute as root of
(sub)tree
29. 29
Decision Tree Construction Algorithm
• Principle
– Basic algorithm (adopted by ID3, C4.5 and CART): a greedy
algorithm
– Tree is constructed in a top-down recursive divide-and-conquer
manner
• Iterations
– At start, all the training tuples are at the root
– Tuples are partitioned recursively based on selected attributes
– Test attributes are selected on the basis of a heuristic or
statistical measure (e.g, information gain)
• Stopping conditions
– All samples for a given node belong to the same class
– There are no remaining attributes for further partitioning –
–
majority voting is employed for classifying the leaf
– There are no samples left
30. 30
March 1, 2023 Data Mining: Concepts and Techniques 30
Decision Tree Induction: Training Dataset
age income student credit_rating buys_computer
<=30 high no fair no
<=30 high no excellent no
31…40 high no fair yes
>40 medium no fair yes
>40 low yes fair yes
>40 low yes excellent no
31…40 low yes excellent yes
<=30 medium no fair no
<=30 low yes fair yes
>40 medium yes fair yes
<=30 medium yes excellent yes
31…40 medium no excellent yes
31…40 high yes fair yes
>40 medium no excellent no
This
follows an
example
of
Quinlan’s
ID3
(Playing
Tennis)
38. 38
Tree Induction
• Greedy strategy.
– Split the records based on an attribute test
that optimizes certain criterion.
• Issues
– Determine how to split the records
• How to specify the attribute test condition?
• How to determine the best split?
– Determine when to stop splitting
39. 39
Choosing an attribute
• Idea: a good attribute splits the examples into subsets
that are (ideally) "all positive" or "all negative"
• Patrons? is a better choice
40. 40
How to determine the Best Split
• Greedy approach:
– Nodes with homogeneous class distribution
are preferred
• Need a measure of node impurity:
C0: 5
C1: 5
C0: 9
C1: 1
Non-homogeneous,
High degree of impurity
Homogeneous,
Low degree of impurity
41. 41
Measures of Node Impurity
• Information Gain
• Gini Index
• Misclassification error
Choose attributes to split to achieve minimum impurity
42. 42
March 1, 2023 Data Mining: Concepts and Techniques 42
Attribute Selection Measure:
Information Gain (ID3/C4.5)
Select the attribute with the highest information gain
Let pi be the probability that an arbitrary tuple in D belongs
to class Ci, estimated by |Ci, D|/|D|
Expected information (entropy) needed to classify a tuple in
D:
Information needed (after using A to split D into v partitions)
to classify D:
Information gained by branching on attribute A
)
(
log
)
( 2
1
i
m
i
i p
p
D
I
)
(
|
|
|
|
)
(
1
j
v
j
j
A D
I
D
D
D
Info
(D)
Info
Info(D)
Gain(A) A
43. 43
Information gain
For the training set, p = n = 6, I(6/12, 6/12) = 1 bit
Consider the attributes Patrons and Type (and others too):
Patrons has the highest IG of all attributes and so is chosen by the DTL
algorithm as the root
bits
0
)]
4
2
,
4
2
(
12
4
)
4
2
,
4
2
(
12
4
)
2
1
,
2
1
(
12
2
)
2
1
,
2
1
(
12
2
[
1
)
(
bits
0541
.
)]
6
4
,
6
2
(
12
6
)
0
,
1
(
12
4
)
1
,
0
(
12
2
[
1
)
(
I
I
I
I
Type
IG
I
I
I
Patrons
IG
44. 44
Example contd.
• Decision tree learned from the 12 examples:
• Substantially simpler than “true” tree---a more complex
hypothesis isn’t justified by small amount of data
45. 45
Measure of Impurity: GINI
(CART, IBM IntelligentMiner)
• Gini Index for a given node t :
(NOTE: p( j | t) is the relative frequency of class j at node t).
– Maximum (1 - 1/nc) when records are equally distributed among
all classes, implying least interesting information
– Minimum (0.0) when all records belong to one class, implying
most interesting information
j
t
j
p
t
GINI 2
)]
|
(
[
1
)
(
C1 0
C2 6
Gini=0.000
C1 2
C2 4
Gini=0.444
C1 3
C2 3
Gini=0.500
C1 1
C2 5
Gini=0.278
46. 46
Splitting Based on GINI
• Used in CART, SLIQ, SPRINT.
• When a node p is split into k partitions (children),
the quality of split is computed as,
where, ni = number of records at child i,
n = number of records at node p.
k
i
i
split i
GINI
n
n
GINI
1
)
(
47. 47
March 1, 2023 Data Mining: Concepts and Techniques 47
Comparison of Attribute Selection Methods
• The three measures return good results but
– Information gain:
• biased towards multivalued attributes
– Gain ratio:
• tends to prefer unbalanced splits in which one
partition is much smaller than the others
– Gini index:
• biased to multivalued attributes
• has difficulty when # of classes is large
• tends to favor tests that result in equal-sized
partitions and purity in both partitions
48. 48
Example Algorithm: C4.5
• Simple depth-first construction.
• Uses Information Gain
• Sorts Continuous Attributes at each node.
• Needs entire data to fit in memory.
• Unsuitable for Large Datasets.
• You can download the software from
Internet
49. 49
Decision Tree Based Classification
• Advantages:
– Easy to construct/implement
– Extremely fast at classifying unknown records
– Models are easy to interpret for small-sized trees
– Accuracy is comparable to other classification
techniques for many simple data sets
– Tree models make no assumptions about the
distribution of the underlying data : nonparametric
– Have a built-in feature selection method that makes
them immune to the presence of useless variables
50. 50
Decision Tree Based Classification
• Disadvantages
– Computationally expensive to train
– Some decision trees can be overly complex that
do not generalise the data well.
– Less expressivity: There may be concepts that are
hard to learn with limited decision trees
51. 51
March 1, 2023 Data Mining: Concepts and Techniques 51
Overfitting and Tree Pruning
• Overfitting: An induced tree may overfit the training
data
– Too many branches, some may reflect anomalies due to noise or outliers
– Poor accuracy for unseen samples
• Two approaches to avoid overfitting
– Prepruning: Halt tree construction early—do not split a node if this would
result in the goodness measure falling below a threshold
• Difficult to choose an appropriate threshold
– Postpruning: Remove branches from a “fully grown” tree—get a sequence of
progressively pruned trees
• Use a set of data different from the training data to decide which is the
“best pruned tree”
52. 52
Rule-Based Classifier
• Classify records by using a collection of
“if…then…” rules
• Rule: (Condition) y
– where
• Condition is a conjunctions of attributes
• y is the class label
– LHS: rule antecedent or condition
– RHS: rule consequent
– Examples of classification rules:
• (Blood Type=Warm) (Lay Eggs=Yes) Birds
• (Taxable Income < 50K) (Refund=Yes) Evade=No
53. 53
Rule-based Classifier (Example)
R1: (Give Birth = no) (Can Fly = yes) Birds
R2: (Give Birth = no) (Live in Water = yes) Fishes
R3: (Give Birth = yes) (Blood Type = warm) Mammals
R4: (Give Birth = no) (Can Fly = no) Reptiles
R5: (Live in Water = sometimes) Amphibians
Name Blood Type Give Birth Can Fly Live in Water Class
human warm yes no no mammals
python cold no no no reptiles
salmon cold no no yes fishes
whale warm yes no yes mammals
frog cold no no sometimes amphibians
komodo cold no no no reptiles
bat warm yes yes no mammals
pigeon warm no yes no birds
cat warm yes no no mammals
leopard shark cold yes no yes fishes
turtle cold no no sometimes reptiles
penguin warm no no sometimes birds
porcupine warm yes no no mammals
eel cold no no yes fishes
salamander cold no no sometimes amphibians
gila monster cold no no no reptiles
platypus warm no no no mammals
owl warm no yes no birds
dolphin warm yes no yes mammals
eagle warm no yes no birds
54. 54
March 1, 2023 Data Mining: Concepts and Techniques 54
age?
student? credit rating?
<=30 >40
no yes yes
yes
31..40
fair
excellent
yes
no
• Example: Rule extraction from our buys_computer decision-tree
IF age = young AND student = no THEN buys_computer = no
IF age = young AND student = yes THEN buys_computer = yes
IF age = mid-age THEN buys_computer = yes
IF age = old AND credit_rating = excellent THEN buys_computer = yes
IF age = young AND credit_rating = fair THEN buys_computer = no
Rule Extraction from a Decision Tree
Rules are easier to understand than large trees
One rule is created for each path from the
root to a leaf
Each attribute-value pair along a path forms a
conjunction: the leaf holds the class prediction
57. 57
Classification(Sınıflandırma)
• IDEA: Build a model based on past data to
predict the class of the new data
• Given a collection of records (training set )
– Each record contains a set of attributes, one of the attributes is
the class.
• Find a model for class attribute as a function of
the values of other attributes.
• Goal: previously unseen records should be
assigned a class as accurately as possible.
58. 58
Hypothesis spaces
How many distinct decision trees with n Boolean attributes?
= number of Boolean functions
= number of distinct truth tables with 2n rows = 22n
• E.g., with 6 Boolean attributes, there are
18,446,744,073,709,551,616 trees
How many purely conjunctive hypotheses (e.g., Hungry Rain)?
• Each attribute can be in (positive), in (negative), or out
3n distinct conjunctive hypotheses
• More expressive hypothesis space
– increases chance that target function can be expressed
– increases number of hypotheses consistent with training set
may get worse predictions
59. 59
Using information theory
• To implement Choose-Attribute in the DTL
algorithm
• Information Content (Entropy):
I(P(v1), … , P(vn)) = Σi=1 -P(vi) log2 P(vi)
• For a training set containing p positive examples
and n negative examples:
n
p
n
n
p
n
n
p
p
n
p
p
n
p
n
n
p
p
I
2
2 log
log
)
,
(
60. 60
Information gain
• A chosen attribute A divides the training set E into
subsets E1, … , Ev according to their values for A, where
A has v distinct values.
• Information Gain (IG) or reduction in entropy from the
attribute test:
• Choose the attribute with the largest IG
v
i i
i
i
i
i
i
i
i
n
p
n
n
p
p
I
n
p
n
p
A
remainder
1
)
,
(
)
(
)
(
)
,
(
)
( A
remainder
n
p
n
n
p
p
I
A
IG
61. 61
Performance measurement
• How do we know that h ≈ f ?
1. Use theorems of computational/statistical learning theory
2. Try h on a new test set of examples
(use same distribution over example space as training set)
Learning curve = % correct on test set as a function of training set size