C4.5 enhances ID3 by making it more robust to noise, able to handle continuous attributes, deal with missing data, and convert decision trees to rules. It avoids overfitting through pre-pruning and post-pruning techniques. When dealing with continuous attributes, it evaluates all possible split points and chooses the optimal one. It treats missing data as a separate value but this is not always appropriate. It generates rules from trees in a greedy manner by pruning conditions to reduce estimated error. The next topic will be on instance-based classifiers.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...Edureka!
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka tutorial on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
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
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.
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...Edureka!
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka tutorial on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
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
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.
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Artificial Intelligence is back, Deep Learning Networks and Quantum possibili...John Mathon
AI has gone through a number of mini-boom-bust periods. The current one may be short lived as well but I have reasons to think AI is finally making some sustained progress that will see its way into mainstream technology.
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
Machine Learning for Incident Detection: Getting StartedSqrrl
This presentation walks you through the uses of machine learning in incident detection and response, outlining some of the basic features of machine learning and specific tools you can use.
Watch the presentation with audio here: https://www.youtube.com/watch?v=4pArapSIu_w
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
1. Introduction to Machine
Learning
Lecture 6
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull
2. Recap of Lecture 4
ID3 is a strong system that
gy
Uses hill-climbing search based on the information gain
measure to sea c through the space o dec s o trees
easu e o search oug e of decision ees
Outputs a single hypothesis.
Never b kt k It converges to locally optimal solutions.
N backtracks. tl ll ti l l ti
Uses all training examples at each step, contrary to methods
that
th t make decisions i
k d ii incrementally.
t ll
Uses statistical properties of all examples: the search is less
sensitive t errors i i di id l t i i examples.
iti to in individual training l
Can handle noisy data by modifying its termination criterion to
accept hypotheses that imperfectly fit the data.
th th th t i f tl th d t
Slide 2
Artificial Intelligence Machine Learning
3. Recap of Lecture 4
However, ID3 has some drawbacks
It
I can only deal with nominal d
l d l ih i l data
It is not able to deal with noisy data sets
It may be not robust in presence of noise
Slide 3
Artificial Intelligence Machine Learning
4. Today’s Agenda
Going from ID3 to C4.5
How C4.5 enhances C4.5 to
Be robust in the presence of noise. Avoid overfitting
Deal with continuous attributes
Deal with missing data
Convert trees to rules
Slide 4
Artificial Intelligence Machine Learning
5. What’s Overfitting?
Overfitting = Given a hypothesis space H, a hypothesis hєH is said to
overfit the training data if there exists some alternative hypothesis h’єH,
such that
h has smaller error than h’ over the training examples, but
h examples
1.
1
h’ has a smaller error than h over the entire distribution of instances.
2.
Slide 5
Artificial Intelligence Machine Learning
6. Why May my System Overfit?
In domains with noise or uncertainty
y
the system may try to decrease the training error by completely
fitting a the training e a p es
g all e a g examples
The learner overfits
to correctly classify
the noisy instances Noisy instances
Occam’s razor: Prefer the
simplest hypothesis that fits
the data with high accuracy
Slide 6
Artificial Intelligence Machine Learning
7. How to Avoid Overfitting?
Ok, my system may overfit… Can I avoid it?
, yy y
Sure! Do not include branches that fit data too specifically
How?
H?
Pre-prune: Stop growing a branch when information becomes
1.
unreliable
li bl
Post-prune: Take a fully-grown decision tree and discard
2.
unreliable parts
li bl
Slide 7
Artificial Intelligence Machine Learning
8. Pre-pruning
Based on statistical significance test
g
Stop growing the tree when there is no statistically significant
assoc a o between any attribute and e class at particular
association be ee a y a bu e a d the c ass a a pa cu a
node
Use all available da a for training a d app y the s a s ca test
a a a ab e data o a g and apply e statistical es
to estimate whether expanding/pruning a node is to produce an
improvement beyond the training set
Most popular test: chi-squared test
ID3 used chi-squared test in addition to information gain
Only statistically significant attributes were allowed to be
selected by information gain procedure
Slide 8
Artificial Intelligence Machine Learning
9. Pre-pruning
Early stopping: Pre-pruning may stop the growth process prematurely
Classic example: XOR/Parity-problem
No individual attribute exhibits any significant association to the class
Structure is only visible in fully expanded tree
Pre-pruning won t
Pre pruning won’t expand the root node
But: XOR-type problems rare in practice
And: pre-pruning faster than post-pruning
x1 x2 Class
1 0 0 0
01 10
2 0 1 1
3 1 0 1
4 1 1 0 00 10
Slide 9
Artificial Intelligence Machine Learning
10. Post-pruning
First, build the full tree
,
Then, prune it
Fully-grown
Fully grown tree shows all attribute interactions
Problem: some subtrees might be due to chance effects
Two pruning operations:
Subtree replacement
1.
Subtree raising
2.
Possible strategies:
error estimation
significance t ti
i ifi testing
MDL principle
Slide 10
Artificial Intelligence Machine Learning
11. Subtree Replacement
Bottom up approach
p pp
Consider replacing a tree after considering all its subtrees
Ex: labor negotiations
Slide 11
Artificial Intelligence Machine Learning
12. Subtree Replacement
Algorithm:
1. Split the data into training and validation set
2. Do until further pruning is harmful:
a. Evaluate impact on the validation set of pruning
each possible node
b. Select th
b S l t the node whose removal most i
d h l t increases
the validation set accuracy
Slide 12
Artificial Intelligence Machine Learning
13. Subtree Raising
Delete node
Redistribute instances
Slower than subtree
replacement
(Worthwhile?)
X
Slide 13
Artificial Intelligence Machine Learning
14. Estimating Error Rates
Ok we can prune. But when?
p
Prune only if it reduces the estimated error
Error on the training data is NOT a useful estimator
Q: Why it would result in very little pruning?
Use hold-out set for pruning
hold out
Training
T ii
Separate a validation set Data set’
Training
g
Use this validation set to
test the improvement Data set
Validation
C4.5 s
C4 5’s method set
Derive confidence interval from training data
Use a heuristic limit derived from this for pruning
limit, this,
Standard Bernoulli-process-based method
Shaky statistical assumptions (based on training data)
y p ( g )
Slide 14
Artificial Intelligence Machine Learning
15. Deal with continuous attributes
When dealing with nominal data
g
We evaluated the grain for each possible value
In
I continuous data, we have infinite values.
ti dt h i fi it l
What should we do?
Continuous-valued attributes may take infinite values, but we
have a limited number of values in our instances (at most N if
we have N instances)
Therefore, simulate that you have N nominal values
Evaluate information gain for every possible split point of the
attribute
Choose the best split point
The information gain of the attribute is the information gain
of the best split
Slide 15
Artificial Intelligence Machine Learning
16. Deal with continuous attributes
Example
Outlook Temperature Humidity Windy Play
Sunny
y 85 85 False No
Sunny 80 90 True No
Overcast 83 86 False Yes
Rainy 75 80 False Yes
… … … … …
Continuous attributes
Slide 16
Artificial Intelligence Machine Learning
17. Deal with continuous attributes
Split on temperature attribute:
64 65 68 69 70 71 72 72 75 75 80 81 83 85
Yes
Y N
No Y
Yes Y
Yes Yes
Y No
N No
N Yes Y
Y Yes Yes
Y No
N Y
Yes Yes N
Y No
E.g.: temperature < 71.5: yes/ , no/2
g te pe atu e 5 yes/4, o/
temperature ≥ 71.5: yes/5, no/3
Info([4,2],[5,3]) = 6/14 info([4,2]) + 8/14 info([5,3]) = 0.939 bits
Place split points halfway between values
Can evaluate all split points in one pass!
Slide 17
Artificial Intelligence Machine Learning
18. Deal with continuous attributes
To speed up
p p
Entropy only needs to be evaluated between points of different
c asses
classes
value 64 65 68 69 70 71 72 72 75 75 80 81 83 85
class Yes X
No Yes Yes Yes No No Yes Yes Yes No Yes Yes No
Potential optimal breakpoints
Breakpoints between values of the same class cannot
be optimal
Slide 18
Artificial Intelligence Machine Learning
19. Deal with Missing Data
Treat missing values as a separate value
g p
Missing value denoted “?” in C4.X
Simple idea: treat missing as a separate value
Q: When this is not appropriate?
A: Wh
A When values are missing d to diff
l i i due different reasons
Example 1: gene expression could be missing when it is very
high or very low
Example 2: field IsPregnant=missing for a male patient should be
treated differently (no) than for a female patient of age 25
(unknown)
Slide 19
Artificial Intelligence Machine Learning
20. Deal with Missing Data
Split instances with missing values into pieces
A piece going down a branch receives a weight proportional to
the popularity of the branch
weights sum to 1
Info gain works with fractional instances
Use sums of weights instead of counts
During classification, split the instance into pieces
in the same way
Merge probability distribution using weights
Slide 20
Artificial Intelligence Machine Learning
21. From Trees to Rules
I finally g a tree from domains with
y got
Noisy instances
Missing l
Mi i values
Continuous attributes
But I prefer rules…
No context dependent
Procedure
Generate a rule for each tree
Get context-independent rules
Slide 21
Artificial Intelligence Machine Learning
22. From Trees to Rules
A procedure a little more sophisticated: C4.5Rules
p p
C4.5rules: greedily prune conditions from each rule if this
reduces its es a ed e o
educes s estimated error
Can produce duplicate rules
Check for this at the end
Then
look at each class in turn
consider the rules for that class
find a “good” subset (guided by MDL)
good
Then rank the subsets to avoid conflicts
Finally, remove rules (greedily) if this decreases error on the
training data
Slide 22
Artificial Intelligence Machine Learning
23. Next Class
Instance-based Classifiers
Slide 23
Artificial Intelligence Machine Learning
24. Introduction to Machine
Learning
Lecture 6
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull