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.
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 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.
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.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
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
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
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# Students can catch up on notes they missed because of an absence.
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# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
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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.
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.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
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
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
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Hypothesis on Different Data Mining AlgorithmsIJERA Editor
In this paper, different classification algorithms for data mining are discussed. Data Mining is about
explaining the past & predicting the future by means of data analysis. Classification is a task of data mining,
which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
four different classification approaches namely Frequency Table, Covariance Matrix, Similarity Functions &
Others. As work for research on classification methods, algorithms like Naive Bayesian, K Nearest Neighbors,
Decision Tree, Artificial Neural Network & Support Vector Machine are studied & examined using benchmark
datasets like Iris & Lung Cancer.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Deployment of ID3 decision tree algorithm for placement predictionijtsrd
This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a students profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
Buddi health class imbalance based deep learningRam Swaminathan
BUDDI Health is a disruptive Deep Learning Platform which helps automate healthcare administrative functions such as Medical Coding, Claims Denial Prediction, Clinical Documentation Improvement and more.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
AI TESTING: ENSURING A GOOD DATA SPLIT BETWEEN DATA SETS (TRAINING AND TEST) ...ijsc
Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.
A New Active Learning Technique Using Furthest Nearest Neighbour Criterion fo...ijcsa
Active learning is a supervised learning method that is based on the idea that a machine learning algorithm can achieve greater accuracy with fewer labelled training images if it is allowed to choose the image from which it learns. Facial age classification is a technique to classify face images into one of the several predefined age groups. The proposed study applies an active learning approach to facial age classification which allows a classifier to select the data from which it learns. The classifier is initially trained using a small pool of labeled training images. This is achieved by using the bilateral two dimension linear discriminant analysis. Then the most informative unlabeled image is found out from the unlabeled pool using the furthest nearest neighbor criterion, labeled by the user and added to the
appropriate class in the training set. The incremental learning is performed using an incremental version of bilateral two dimension linear discriminant analysis. This active learning paradigm is proposed to be applied to the k nearest neighbor classifier and the support vector machine classifier and to compare the performance of these two classifiers.
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
Materi webinar yang diselenggarakan oleh PHP Indonesia secara live di Facebook PHP-ID. Topik yang dibahas mengenai Machine Learning dengan PHP, baik sisi konsep maupun implementasinya.
Webinar Data Mining dengan Rapidminer | Universitas Budi LuhurAchmad Solichin
Materi Webinar Data Mining dengan Rapidminer di Universitas Budi Luhur yang diselenggarakan oleh mahasiswa S2 Ilmu Komputer Universitas Budi Luhur pada hari Jumat, 8 Januari 2021
TREN DAN IDE RISET BIDANG DATA MINING TERBARUAchmad Solichin
Presentasi ini menyajikan Tren dan Ide Riset Bidang DATA MINING Tahun ini. Wajib disimak! Disertai juga puluhan Contoh Paper Penelitian Terkini di bidang Data Mining. Saya menjelaskan banyak ide penelitian untuk skripsi, tesis, disertasi, dll. Simak sampe akhir ya.
Materi Seminar: Artificial Intelligence dengan PHPAchmad Solichin
Materi Seminar: Artificial Intelligence dengan PHP ini disampaikan pada Seminar online yang diselenggarakan oleh Lab ICT Universitas Budi Luhur pada hari Rabu, 12 Agustus 2020
Slide ini menjelaskan mengenai konsep dan langkah-langkah Algoritma Depth First Search (BFS) pada Graph.
Slide disusun oleh Achmad Solichin (http://achmatim.net)
Slide ini menjelaskan mengenai konsep dan langkah-langkah Algoritma Breadth First Search (BFS) pada Graph.
Slide disusun oleh Achmad Solichin | http://achmatim.net
Materi seminar ini menjelaskan mengenai konsep dasar computer vision dan aplikasinya di era Industri 4.0. Materi seminar ini disampaikan pada acara Seminar Tahunan IT yang diselenggarakan oleh Lab ICT Universitas Budi Luhur
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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05 Classification And Prediction
1. Objectives
Introduction
What is Classification?
Classification vs Prediction
Supervised and Unsupervised Learning
Data Preparation
D t P ti
Classification Accuracy
Classification and Prediction
Lecture 5/DMBI/IKI83403T/MTI/UI ID3 Algorithm
Yudho Giri Sucahyo, Ph.D, CISA (yudho@cs.ui.ac.id)
Information Gain
Faculty of Computer Science, University of Indonesia Bayesian Classification
Predictive Modelling
2 University of Indonesia
Introduction What is Classification? – A two step process
two-step
Databases are rich with hidden information that can be used Model construction:
for making intelligent business decisions. Each tuple is assumed to belong to a predefined class, as
Classification and prediction can be used to extract models determined by one of the attributes, called the class label.
describing i
d ibi important d classes or to predict f
data l di future data
d Data tuples are also referred to as samples, examples, or objects.
trends.
All tuples used for construction is called training set.
Classification predicts categorical labels. Ex: categorize bank
labels
loan applications safe or risky. Since the class label of each training sample is provided
Prediction models continuous-valued functions. Ex: predict the supervised learning. In clustering (unsupervised learning),
expenditures of potential customers on computer equipment the l labels f
th class l b l of each training sample i not known, and th
ht i i l is t k d the
given their income and occupation. number or set of classes to be learned may not be known in
Typical Applications: advance.
Credit approval, target marketing, The model is represented in the following forms:
Medical diagnosis, treatment effectiveness analysis
g , y Classification rules, (
, (IF-THEN statements), decision tree, mathematical
), ,
formulae
University of Indonesia
4 University of Indonesia
2. What is Classification? – A two step process (2)
two-step Classification Process (1)
The model is used for classifying future or Classification
unknown objects. Algorithms
Training
First, the predictive accuracy of the model is estimated Data
The known label of test sample is compared with the classified result
from the model.
Accuracy rate is the percentage of test set samples that are correctly
classified by the model. NAM E RANK YEARS TENURED Classifier
(Model)
Test set is independent of training set otherwise over-fitting (it may M ike Assistant Prof 3 no
have incorporated some particular anomalies of the training data that M ary Assistant Prof 7 yes
are not present in the overall sample population) will occur. Bill Professor 2 yes
If the accuracy of the model is considered acceptable the
acceptable, Jim Associate Prof 7 yes
model can be used to classify future objects for which the Dave Assistant Prof 6 no
IF rank = ‘professor’
OR years > 6
class label is not known (unknown, p
( previously unseen data).
y ) Anne Associate Prof 3 no THEN tenured = ‘yes’
yes
6
5 University of Indonesia University of Indonesia
Classification Process (2) What is Prediction?
Prediction is similar to classification
Classifier
First, construct model.
Second, use model to predict future or unknown objects
p j
Major method for prediction is regression:
Testing Linear and multiple regression
Data Unseen Data
Non-liner regression
(Jeff, Professor, 4) Prediction is different from classification
Classification refers to predict categorical class label.
NAM E RANK YEARS TENURED
Tom Assistant Prof 2 no Tenured? Prediction refers to predict continuous value.
M erlisa Associate Prof 7 no
George Professor 5 yes
Joseph Assistant Prof 7 yes
es
7
University of Indonesia
8 University of Indonesia
3. Classification vs Prediction Supervised vs Unsupervised Learning
Sending out promotional literature to every new Supervised learning (classification)
customer in the database can be quite costly. A more cos- Supervision: The training data (observations, measurements,
efficient method would be to target only those new etc.) are accompanied by labels indicating the class of the
) p y g
customers who are likely to purchase a new computer
h lik l h observations
classification.
Based on the training set to classify new data
g y
Predict h
P d the number of major purchases that a customer
b f h h
will make during a fiscal year prediction. Unsupervised learning (clustering)
We are given a set of measurements, observations etc with
measurements observations,
the aim of establishing the existence of classes or clusters in
the data
No training data, or the “training data” are not accompanied
by class labels
9 University of Indonesia
10 University of Indonesia
Issues – Data Preparation Issues – Data Preparation
Data preprocessing can be used to help improve the Data Transformation
accuracy, efficiency, and scalability of the classification or Data can be generalized to higher-level concepts.
prediction process. Useful fot continuous-valued attributes.
Data Cleaning Income can be generalized low, medium, high.
Remove/reduce noise and the treatment of missing values Street city.
Relevance Analysis Generalization compresses the original training data, fewer
Many of the attributes in the data may be irrelevant to the input/output operations may be involved during learning.
classification or prediction task. Ex: data recording the day of When i
Wh using neural networks (or other methods involving
l t k ( th th d i l i
the week on which a bank loan application was filed is unlikely distance measurements), data may also be normalized.
to be relevant to the success of the application
application.
Other attributes may be redundant.
This step is known as feature selection
selection.
11 University of Indonesia
12 University of Indonesia
4. Comparing Classification Method Classification Accuracy: Estimating Error Rates
Predictive accuracy Partition: Training-and-testing
Speed and scalability use two independent data sets, e.g., training set (2/3), test
time to construct the model
set(1/3)
time to use the model
used for data set with large number of samples
Robustness
handling noise and missing values Cross-validation
Scalability divide the data set into k subsamples
efficiency in large databases (not memory resident data)
use k-1 subsamples as training data and one sub-sample as test
p g p
Interpretability: data --- k-fold cross-validation
the level of understanding and insight provided by the model
for data set with moderate size
Goodness of rules
decision tree size Bootstrapping (leave-one-out)
the compactness of classification rules for small size data
13 University of Indonesia
14 University of Indonesia
What is a decision tree? Training Dataset
A decision tree is a flow-chart-like tree structure. An Example
Outlook Tempreature Humidity Windy Class
sunny hot high false N
Internal node denotes a test on an attribute from Quinlan’s sunny hot high true N
Branch represents an outcome of the test ID3 overcast hot high
g false P
All tuples in branch have the same value for the tested rain mild high false P
attribute. rain cool normal false P
Leaf node represents class label or class label distribution. rain cool normal tr e
true N
overcast cool normal true P
To classify an unknown sample, the attribute values of the sunnyy mild high
g false N
sample are tested against the decision tree. A path is traced sunny cool normal false P
from the root to a leaf node that holds the class prediction rain mild normal false P
for h
f that sample. l sunny mild
ild normal t
l true P
overcast mild high true P
Decision trees can easily be converted to classification rules. overcast hot normal false P
rain mild high true N
15 University of Indonesia
16 University of Indonesia
5. A Sample Decision Tree Decision-Tree
Decision Tree Classification Methods
The basic top-down decision tree generation approach
Outlook
usually consists of two phases:
sunny
Tree construction
overcast
overcast rain
At start, all the training examples are at the root.
Partition examples recursively based on selected
humidity P windy
attributes.
Tree pruning
high normal true false
Aiming at removing tree branches that may lead to errors
when classifying t t d t (t i i d t may contain noise,
h l if i test data (training data t i i
N P N P
outliers, …)
17 University of Indonesia
18 University of Indonesia
Choosing Split Attribute –
ID3 Algorithm Information Gain (ID3/C4 5) (1)
(ID3/C4.5)
All attributes are categorical Assume all attributes to be categorical (discrete-values).
Create a node N; Continuous-valued attributes must be discretized.
if samples are all of the same class C, then
Used to select the test attribute at each node in the tree.
return N as a leaf node labeled with C
if attribute-list is empty then Also called measure of the goodness of split.
return N as a leaf node labeled with the most common class The attribute with the highest information gain is chosen
g g
select split-attribute with highest information gain as the test attribute for the current node.
label N with the split-attribute
for
f each value Ai of split-attribute, grow a branch from Node N
h l f lit tt ib t b hf N d
let Si be the branch in which all tuples have the value Ai for split- attribute
if Si is empty then
attach a leaf labeled with the most common class
Else recursively run the algorithm at Node Si
until all branches reach leaf nodes
19 University of Indonesia
20 University of Indonesia
6. Information Gain (ID3/C4 5) (2)
(ID3/C4.5) Information Gain (ID3/C4 5) (3)
(ID3/C4.5)
Assume that there are two classes, P and N. The attribute A is selected such that the information gain
Let h
L the set of examples S contain p elements of class P and n
f l l f l d gain(A) = I(p, n) - E(A)
elements of class N.
The amount of information needed to decide if an arbitrary
information, is maximal, that is, E(A) is minimal since I(p, n) is the same to all
, , ( ) (p, )
example in S belong to P or N is defined as attributes at a node.
p p n n In the given sample data, attribute outlook is chosen to split at
data
I ( p, n ) = − lo g 2 − lo g 2
p+n p+n p+n p+n
the root :
Assume that using attribute A as the root in the tree will partition
S in sets {S1, S2 , …, Sv}
}. gain(outlook) = 0 246
i ( tl k) 0.246
If Si contains pi examples of P and ni examples of N, the information gain(temperature) = 0.029
needed to classify objects in all subtrees Si :
gain(humidity) = 0.151
v pi + ni gain(windy) = 0.048
E( A) = ∑ I ( pi , ni)
i =1 p+n
21 University of Indonesia
22 University of Indonesia
Information Gain (ID3/C4 5) (3)
(ID3/C4.5) Information Gain (ID3/C4 5) (4)
(ID3/C4.5)
Examples: Next, compute the entropy of each attribute. Let’s start with the
See Table 7.1. attribute age.
ib
For age = “<= 30”: s11 = 2 s21 = 3 I (s11, s21) = 0.971
Class label: buys_computer. Two values:YES, NO.
For age = “31 40”: s12 = 4 s22 = 0 I (s12, s22) = 0
31..40 :
m = 2. C1 correspond to yes, C2 correspond to no.
For age = “>40”: s13 = 3 s23 = 2 I (s13, s23) = 0.971
9 samples of class yes and 5 samples of class no.
Using equation (7.2), the expected information needed to classify
(7 2)
Compute the expected information needed to classify a given
a given sample if the samples are partitioned by age is
sample 5 4 5
E ( age ) = I ( s 11 , s 21 ) + I ( s 12 , s 22 ) + I ( s 13 , s 23 ) = 0 . 694
9 9 5 5 14 14 14
I ( s1 , s 2 ) = I (9,5) = − log − log = 0 .940 Hence, the gain in information from such a partitioning:
14 2 14 14 2 14
Gain(age) = I (s1, s2) – E (age) = 0.246
( g ) ( ( g )
Similarly, we can compute Gain(income) = 0.029, Gain(student) =
0.151, Gain(Credit_rating) = 0.048.
23 University of Indonesia
24 University of Indonesia
7. How to use a tree? Tree Pruning
Directly A decision tree constructed using the training data may have
test the attribute value of unknown sample against the tree. too many branches/leaf nodes.
/ f
A path is traced from root to a leaf which holds the label Caused by noise, overfitting
Indirectly May
M result poor accuracy for unseen samples
l f l
decision tree is converted to classification rules Prune the tree: merge a subtree into a leaf node.
one rule is created for each path from the root to a leaf Using
U i a set of d different from the training data.
f data diff f h i i d
IF-THEN is easier for humans to understand At a tree node, if the accuracy without splitting is higher than
Example:
the accuracy with splitting replace the subtree with a leaf node
splitting, node,
label it using the majority class.
IF age = “<=30” AND student = “no” THEN buys_computer = “no”
Pruning Criterion:
Pessimistic pruning: C4.5
MDL: SLIQ and SPRINT
Cost complexity pruning: CART
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26 University of Indonesia
Classification and Databases Classifying Large Dataset
Classification is a classical problem extensively studied by Decision trees seem to be a good choice
statisticians relatively faster learning speed than other classification
AI, especially machine learning researchers methods
Database researchers re-examined the problem in the can be converted into simple and easy to understand
classification rules
context of large databases
can be used to generate SQL queries for accessing databases
most previous studies used small size data, and most
algorithms are memory resident has comparable classification accuracy with other methods
Recent data mining research contributes to Classifying data sets with millions of examples and a few
data-sets
Scalability hundred even thousands attributes with reasonable
Generalization-based classification
speed.
speed
Parallel and distributed processing
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28 University of Indonesia
8. Scalable Decision Tree Methods Previous Efforts on Scalability
Most algorithms assume data can fit in memory. Incremental tree construction (Quinlan’86)
( )
Data mining research contributes to the scalability issue, using partial data to build a tree.
especially for decision trees.
p y testing other examples and those mis-classified ones are used
g p
to rebuild the tree interactively.
Successful examples
Data reduction (Cattlet’91)
SLIQ (EDBT’96 -- Mehta et al ’96)
(EDBT 96 al. 96)
reducing data size by sampling and discretization.
SPRINT (VLDB96 -- J. Shafer et al.’96)
still a main memory algorithm.
PUBLIC (VLDB98 -- Rastogi & Shim 98)
Shim’98)
Data partition and merge (Chan and Stolfo’91)
RainForest (VLDB98 -- Gehrke, et al.’98)
partitioning data and building trees for each partition.
merging multiple trees into a combined tree.
experiment results indicated reduced classification accuracy.
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30 University of Indonesia
Presentation of Classification Rules Other Classification Methods
Bayesian Classification
Neural Networks
Genetic Algorithm
Rough Set Approach
k-Nearest Neighbor Classifier
Case-Based Reasoning (CBR)
Fuzzy Logic
Support Vector Machine (SVM)
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32 University of Indonesia
9. Bayesian Classification Bayes Theorem (1)
Bayesian classifiers are statistical classifiers. Let X be a data sample whose class label is unknown.
They can predict class membership probabilities, such as Let H be some hypothesis, such as that the data sample X
the probability that a given sample belongs to a particular belongs to a specified class C.
class. We want to determine P(H|X), the probability the the
Bayesian classification is based on Bayes theorem. hypothesis H holds given the observed data sample X.
Naive Bayesian Classifier is comparable in performance P(H|X) is the posterior probability or a posteriori
with decision tree and neural network classifiers. probability, of H conditioned on X.
Bayesian classifiers also have high accuracy and speed Support the world of data samples consists of fruits, described
when applied to large databases. by their color and shape., Suppose that X is red and round, and
that H i the h
h is h hypothesis that X i an apple. Th P(H|X)
h i h is l Then
reflects our confidence that X is an apple given that we have
seen that X is red and round.
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34 University of Indonesia
Bayes Theorem (2) Predictive Modeling in Databases
P(H) is the prior probability or a priori probability, of H. What if we would like to predict a continuous value,
The probability that any given data sample is an apple, rather than a categorical label?
regardless of how the data sample looks. Prediction of continuous values can be modeled by statistical
The posterior probability is based on more information techniques of regression.
h i f i
(such as background knowledge) than the prior Example:
probability which i i d
b bili hi h is independent of X
d f X. A m dle t predict the salary of c lle e graduates with 10 years of
modle to redict salar f college rad ates ith ears f
work experience.
Bayes theorem is P(H | X ) = P( X | H )P(H ) Potential sales of a new product given its price.
P( X )
(X
Many problems can be solved by linear regression.
See example 7.4 for example on Naive Bayesian Software packages for solving regression problems:
Classification. SAS, SPSS, S-Plus
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36 University of Indonesia
10. Linear Regression Prediction: Numerical Data
Data are modeled using a straight line.
The simplest form of regression
Bivariate liner regressions models a random variable Y
g
(called a response variable), as a linear function of another
random variable, X (called a predictor variable)
Y=α+βX
See Example 7.6 for an example of linear regression.
p p g
Other regression models
Multiple regression
Log-linear models
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Prediction: Categorical Data Conclusion
Classification is an extensively studied problem (mainly in
statistics, machine learning & neural networks)
Classification is probably one of the most widely used data
mining techniques with a lot of applications.
Scalability is still an important issue for database applications.
Combining classification with database techniques should be a
g q
promising research topic.
Research Direction: Classification of non relational data e g
non-relational data, e.g.,
text, spatial, multimedia, etc..
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40 University of Indonesia
11. References References (2)
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L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth M. Mehta, R. Agrawal, and J. Rissanen. SLIQ : A fast scalable classifier for data mining. In Proc.
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P. K. Chan and S. J. Stolfo. Learning arbiter and combiner trees from partitioned data for scaling S. K. Murthy, Automatic Construction of Decision Trees from Data: A Multi-Diciplinary Survey,
Data Mining and Knowledge Discovery 2(4): 345-389, 1998
machine learning. In Proc. 1st Int. Conf. Knowledge Discovery and Data Mining (KDD'95), pages
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J. R. Quinlan. Bagging, boosting, and c4.5. In Proc. 13th Natl. Conf. on Artificial Intelligence
gg g g g
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l C d A 1995 (AAAI'96), 725-730, Portland, OR, Aug. 1996.
U. M. Fayyad. Branching on attribute values in decision tree generation. In Proc. 1994 AAAI Conf., R. Rastogi and K. Shim. Public: A decision tree classifer that integrates building and pruning. In
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, , Proc. 1998 Int. Conf. Very Large Data Bases, 404-415, New York, NY, August 1998.
J. Shafer, R. Agrawal, and M. Mehta. SPRINT : A scalable parallel classifier for data mining. In
J. Gehrke, R. Ramakrishnan, and V. Ganti. Rainforest: A framework for fast decision tree
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construction of large datasets. In Proc. 1998 Int. Conf. Very Large Data Bases, pages 416-427, New
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York, NY, August 1998. Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan
Kaufman, 1991.
M. Kamber, L. Winstone, W. Gong, S. Cheng, and J. Han. Generalization and decision tree induction:
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Engineering (RIDE'97), pages 111-120, Birmingham, England, April 1997.
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