This document provides an overview of classification techniques. It defines classification as assigning records to predefined classes based on their attribute values. The key steps are building a classification model from training data and then using the model to classify new, unseen records. Decision trees are discussed as a popular classification method that uses a tree structure with internal nodes for attributes and leaf nodes for classes. The document covers decision tree induction, handling overfitting, and performance evaluation methods like holdout validation and cross-validation.
Decision tree in artificial intelligenceMdAlAmin187
Decision tree.
Decision Tree that based on artificial intelligence. The main ideas behind Decision Trees were invented more than 70 years ago, and nowadays they are among the most powerful Machine Learning tools.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
ID3, C4.5 :used to generate a decision tree developed by Ross Quinlan typically used in the machine learning and natural language processing domains, overview about these algorithms with illustrated examples
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. 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.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course 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.
Learn more at: https://www.simplilearn.com
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
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
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Classification: Basic Concepts and Decision Treessathish sak
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.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Decision tree in artificial intelligenceMdAlAmin187
Decision tree.
Decision Tree that based on artificial intelligence. The main ideas behind Decision Trees were invented more than 70 years ago, and nowadays they are among the most powerful Machine Learning tools.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
ID3, C4.5 :used to generate a decision tree developed by Ross Quinlan typically used in the machine learning and natural language processing domains, overview about these algorithms with illustrated examples
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. 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.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course 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.
Learn more at: https://www.simplilearn.com
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
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
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Classification: Basic Concepts and Decision Treessathish sak
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.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Efficient classification of big data using vfdt (very fast decision tree)eSAT Journals
Abstract
Decision Tree learning algorithms have been able to capture knowledge successfully. Decision Trees are best considered when
instances are described by attribute-value pairs and when the target function has a discrete value. The main task of these
decision trees is to use inductive methods to the given values of attributes of an unknown object and determine an
appropriate classification by applying decision tree rules. Decision Trees are very effective forms to evaluate the performance
and represent the algorithms because of their robustness, simplicity, capability of handling numerical and categorical data,
ability to work with large datasets and comprehensibility to a name a few. There are various decision tree algorithms available
like ID3, CART, C4.5, VFDT, QUEST, CTREE, GUIDE, CHAID, CRUISE, etc. In this paper a comparative study on three of
these popular decision tree algorithms - (Iterative Dichotomizer 3), C4.5 which is an evolution of ID3 and VFDT (Very
Fast Decision Tree has been made. An empirical study has been conducted to compare C4.5 and VFDT in terms of accuracy
and execution time and various conclusions have been drawn.
Key Words: Decision tree, ID3, C4.5, VFDT, Information Gain, Gain Ratio, Gini Index, Over−fitting.
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
— The automation of fault detection in material
science is getting popular because of less cost and time. Steel
plates fault detection is an important material science problem.
Data mining techniques deal with data analysis of large data.
Decision trees are very popular classifiers because of their simple
structures and accuracy. A classifier ensemble is a set of
classifiers whose individual decisions are combined in to classify
new examples. Classifiers ensembles generally perform better
than single classifier. In this paper, we show the application of
decision tree ensembles for steel plates faults prediction. The
results suggest that Random Subspace and AdaBoost.M1 are the
best ensemble methods for steel plates faults prediction with
prediction accuracy more than 80%. We also demonstrate that if
insignificant features are removed from the datasets, the
performance of the decision tree ensembles improve for steel
plates faults prediction. The results suggest the future
development of steel plate faults analysis tools by using decision
tree ensembles.
4. Introduction (1/4) Classification: Definition 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. A test set is used to determine the accuracy of the model. Classification Model Output Class Label Input Attribute set Chapter 4: Classification 11 December 2009 4/46
5. Introduction(2/4) Classification: Two step process: 1-learning step: Training data are analyzed by classification algorithm and a model (classifier) is learned. 2- Classification: Test data are used to estimate the accuracy of the classification rules. Usually the given data set is divided into training and test sets. Chapter 4: Classification 11 December 2009 5/46
6. Introduction (3/4) Examples of Classification: Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc Chapter 4: Classification 11 December 2009 6/46
7. Introduction (4/4) Classification Techniques: Decision Trees Based Methods. Rule Based Methods. Neural Networks. Naïve Bayes and Bayesian Belief Networks. Support Vector Machines. Chapter 4: Classification 11 December 2009 7/46
9. General Approach To Solving a Classification Problem (1/2) General Approach for building a classification model. Chapter 4: Classification 11 December 2009 9/46
10. General Approach To Solving a Classification Problem (2/2) Performance evaluation. Evaluating the performance of a classification model is based on the counts of test records correctly and incorrectly predicted by the model. Although a confusion matrix provides the information needed to determine how well a classification model perform, summarizing this information with a single number would make it more convenient to compare the performance to a different models. Confusion matrix for a 2-class problem Chapter 4: Classification 11 December 2009 10/46
12. Decision Tree Induction (1/15) What is a decision tree? A decision tree is a flowchart-like tree structure. Each internal node (none leaf node) denotes a test on an attribute, each branch represents an outcome of the test and each leaf node (terminal node) holds a class label. Single, Divorced Internal node MarSt Married Root node Refund NO No Yes TaxInc NO > 80K < 80K YES NO Leaf nodes Chapter 4: Classification 11 December 2009 12/46
13. Decision Tree Induction (2/15) How to build a decision tree? Let Dt be the set of training records that reach a node t General Procedure: If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt If Dt is an empty set, then t is a leaf node labeled by the default class, yd If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset. Dt ? Chapter 4: Classification 11 December 2009 13/46
14. Decision Tree Induction (3/15) How to build a decision tree? Tree induction: Greedy strategy. Split the record based on an attribute test that optimizes certain condition. Tree induction issues: Determine how to split the record? How to specify the attribute test condition? How to determine the best split? Determine when to stop splitting. Chapter 4: Classification 11 December 2009 14/46
15. Decision Tree Induction (4/15) How to specify test condition? Depends on attribute types Nominal. Ordinal. Continuous. Depends on number of ways to split. 2-way split. Multi-way split. Chapter 4: Classification 11 December 2009 15/46
16. Decision Tree Induction (5/15) Splitting based on nominal attributes. Multi-way split Use as many partition as distinct values. Binary split. Divides the values into two subsets. CarType Family Luxury Sports CarType CarType {Family, Luxury} {Sports, Luxury} {Sports} {Family} OR Chapter 4: Classification 11 December 2009 16/46
17. Decision Tree Induction (6/15) Splitting based on ordinal attributes. Multi-way split Use as many partition as distinct values. Binary split. Divides the values into two subsets. as long as it doesn’t violate the order property of the attribute Size Small Large Medium Size Size {Small, Medium} {Medium, Large} {Large} {Small} OR Chapter 4: Classification 11 December 2009 17/46
18. Decision Tree Induction (7/15) Splitting based on continuous attributes. Multi-way split Must consider all possible test for continuous values. One approach, Discretization. Binary split. The test condition can be expressed as a comparison test. (A < v) or (A v) Chapter 4: Classification 11 December 2009 18/46
19. Decision Tree Induction (8/15) How to determine the best split? Attribute Selection Measure. A heuristic for selecting the splitting criterion that best separate a given data set. Information gain. Gain Ratio. Chapter 4: Classification 11 December 2009 19/46
20. Decision Tree Induction (9/15) Information Gain. Used by ID3 algorithm as its attribute selection measure. Select the attribute with the heights information gain. 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 Chapter 4: Classification 11 December 2009 20/46
21. Decision Tree Induction (10/15) Information Gain. 14 record Class “Yes”=9 records. Class “No”= 5 records. Similarly, Chapter 4: Classification 11 December 2009 21/46
22. Decision Tree Induction (11/15) Information Gain. age? senior youth Middle age Yes Chapter 4: Classification 11 December 2009 22/46
27. Decision Tree Induction (14/15) Comparing attribute selection measures Information gain: biased towards multi-valued attributes. Gain ratio: tends to prefer unbalanced splits in which one partition is much smaller than the others. Chapter 4: Classification 11 December 2009 24/46
28. Decision Tree Induction (15/15) Decision Tree Induction Advantages: Inexpensive to construct. Easy to interpret for small-sized trees. Extremely fast at classifying unknown records Disadvantages: decision tree could be suboptimal (i.e., over fitting) Chapter 4: Classification 11 December 2009 25/46
30. Model Overfitting (1/5) Model Overfitting: Type of errors committed by a classification model: Training errors. Number of misclassification errors committed on training record. Generalization error. The expected error of the model on previously unseen records. Good model must have low training error as well as low generalization error. The model that fit the training data too well can have a poorer generalization error than a model with a high training error. Chapter 4: Classification overfitting 11 December 2009 27/46
31. Model Overfitting (2/5) Reasons of overfitting The presence of Noisein the dataset. Chapter 4: Classification 11 December 2009 28/46
32. Model Overfitting (2/5) Reasons of overfitting The presence of Noisein the dataset. Chapter 4: Classification Misclassified 11 December 2009 29/46
33. Model Overfitting(3/5) Reasons of overfitting Lack of Representative Samples. Chapter 4: Classification Misclassified 11 December 2009 30/46
34. Model Overfitting(4/5) Handling overfitting Pre-Pruning (Early Stopping Rule) Stop the algorithm before it becomes a fully-grown tree Typical stopping conditions for a node: Stop if all instances belong to the same class Stop if all the attribute values are the same More restrictive conditions: Stop if number of instances is less than some user-specified threshold Stop if class distribution of instances are independent of the available features (e.g., using 2 test) Stop if expanding the current node does not improve impurity measures (e.g., Gini or information gain). Chapter 4: Classification 11 December 2009 31/46
35. Model Overfitting(5/5) Handling overfitting Post-pruning Grow decision tree to its entirety Trim the nodes of the decision tree in a bottom-up fashion If generalization error improves after trimming, replace sub-tree by a leaf node. Class label of leaf node is determined from majority class of instances in the sub-tree Chapter 4: Classification In practice , Post-Pruning is preferable since early pruning can “stop too early” 11 December 2009 32/46
37. Performance Evaluation(1/3) Holdout Method Partition: Training-and-testing use two independent data sets, e.g., training set (2/3), test set (1/3) used for data set with large number of samples Chapter 4: Classification 30% Divide randomly Available examples Training Set used to develop one tree check accuracy 11 December 2009 34/46
38. Performance Evaluation(2/3) Cross-Validation divide the data set into k subsamples use k-1 subsamples as training data and one sub-sample as test data k-fold cross-validation used for data set with moderate size 10-fold cross-validation the standard and most popular technique of estimating a classifier accuracy Chapter 4: Classification Available examples 10% 90% Test Set Training Set used to develop 10 different trees check accuracy 11 December 2009 35/46
39. Performance Evaluation(3/3) Bootstrapping Based on the sampling with replacement The initial dataset is sampled N times N : the total number of samples in the dataset, with replacement, to form another set of N samples for training. Since some samples in this new "set" will be repeated, so it means that some samples from the initial dataset will not appear in this training set. These samples will form a test set. Used for small size dataset. Chapter 4: Classification 11 December 2009 36/46
41. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Start from the root of tree. Chapter 4: Classification 11 December 2009 38/46
42. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Chapter 4: Classification 11 December 2009 39/46
43. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Chapter 4: Classification 11 December 2009 40/46
44. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Chapter 4: Classification 11 December 2009 41/46
45. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Chapter 4: Classification 11 December 2009 42/46
46. Summary Refund Yes No MarSt NO Married Single, Divorced TaxInc NO < 80K > 80K YES NO Apply model to test data Test Data Assign Cheat to “No” Chapter 4: Classification 11 December 2009 43/46
48. Summary Classification is one of the most important technique in detaining. Have so much application in real world. Decision tree Powerful classification technique. Decision trees are easy to understand. Strength: Easy to understand, fast in classifying records. Weakness: Suffer from oversetting. Large tree size cause some memory handling issue Handling overfitting: Pruning. Evaluation methods Chapter 4: Classification 11 December 2009 45/46