Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Machine Learning Performance Evaluation: Tips and Pitfalls - Jose Hernandez O...PAPIs.io
Beginners in machine learning usually presume that a proper assessment of a predictive model should simply comply with the golden rule of evaluation (split the data into train and test) in order to choose the most accurate model, which will hopefully behave well when deployed into production. However, things are more elaborate in the real world. The contexts in which a predictive model is evaluated and deployed can differ significantly, not coping well with the change, especially if the model has been evaluated with a performance metric that is insensitive to these changing contexts. A more comprehensive and reliable view of machine learning evaluation is illustrated with several common pitfalls and the tips addressing them, such as the use of probabilistic models, calibration techniques, imbalanced costs and visualisation tools such as ROC analysis.
Jose Hernandez Orallo, Ph.D. is a senior lecturer at Universitat Politecnica de Valencia. His research areas include: Data Mining and Machine Learning, Model re-framing, Inductive Programming and Data-Mining, and Intelligence Measurement and Artificial General Intelligence.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
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.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Machine Learning Performance Evaluation: Tips and Pitfalls - Jose Hernandez O...PAPIs.io
Beginners in machine learning usually presume that a proper assessment of a predictive model should simply comply with the golden rule of evaluation (split the data into train and test) in order to choose the most accurate model, which will hopefully behave well when deployed into production. However, things are more elaborate in the real world. The contexts in which a predictive model is evaluated and deployed can differ significantly, not coping well with the change, especially if the model has been evaluated with a performance metric that is insensitive to these changing contexts. A more comprehensive and reliable view of machine learning evaluation is illustrated with several common pitfalls and the tips addressing them, such as the use of probabilistic models, calibration techniques, imbalanced costs and visualisation tools such as ROC analysis.
Jose Hernandez Orallo, Ph.D. is a senior lecturer at Universitat Politecnica de Valencia. His research areas include: Data Mining and Machine Learning, Model re-framing, Inductive Programming and Data-Mining, and Intelligence Measurement and Artificial General Intelligence.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
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.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
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
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Linear regression with gradient descentSuraj Parmar
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
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
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
Linear regression with gradient descentSuraj Parmar
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Enhancement of Palmprint using Median Filter for Biometrics ApplicationMangilal Saraswat
Presentation based on the research paper:
Enhancement of Palmprint using Median Filter for Biometrics Application
Authors: Saravanan Chandran
Publication date: 2014/2/15
Conference: International Conference on Advances in Science and Technology (ICAST2014)
Pages: 16-18
Publisher: Science Publications
Complete details is here: http://scholar.google.com/citations?user=APuJ3xMAAAAJ
Very long instruction word or VLIW refers to a processor architecture designed to take advantage of instruction level parallelism
This type of processor architecture is intended to allow higher performance without the inherent complexity of some other approaches.
Supervised learning is a machine learning paradigm where the algorithm is trained on a labeled dataset, learning patterns and relationships between input features and corresponding output labels to make accurate predictions on new, unseen data. It involves a teacher-supervisor relationship, where the algorithm strives to minimize the error between its predictions and the actual outcomes during training.
Invited lecture on Machine Learning in Medicine at the joint "Integrated Omics" course of Hanze University and University Hospital UMCG, Groningen, The Netherlands
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
2. Plan
1. Presentation on Multiclass Classification
a. Error Rates and the Bayes Classifier
b. Gaussian and Linear Classifiers. Linear Discriminant Analysis.
Logistic Regression;
c. Multi-class classification models and methods;
d. Multi-class strategies: one-versus-all, one-versus-one, error-
correction-codes
2. Linear Classifiers and Multi-classification Tutorial
3. In-class exercise
1. Multilabel Classification format
2. Classifier Comparison
3. LDA as dimensionality reduction
4. LDA vs PCA
5. Logistic Regression for 3 classes
6. Linear models
7. LDA and QDA
8. Naive Regression
9. Cross Validation in Python
References
9. Naive Bayes
Pros:
1. Fast
2. Prevent curse of dimensionality
3. Decent classifier for several
tasks (e.g. text classification)
4. Inherently multiclass
Cons:
1. Bad estimator of probabilities to
the class.
12. Linear/Quadratic Discriminant Analysis (LDA/QDA)
● LDA = each class has the
same covariance equals to
averaged covariance of the
classes
● QDA = each class has its
own covariance
13. Linear/Quadratic Discriminant Analysis (LDA/QDA)
Pros:
1. Closed-Form solution
2. Inherently Multiclass
3. No hyperparameters tuning
4. Can be used as dimensionality
reduction
Cons:
1. Assume unimodal Gaussian
distribution for each class
2. Cannot reduce dimensions to
more than the number of
classes.
3. Not useful if “information” is in
data variance instead of the
mean of classes.
18. Stochastic Gradient Descent (SGD)
Practical Tips:
● Scale data so that each dimension has unit variance and zero
mean. StandardScaler() in Python.
● Empirically, n_iter = np.ceil(10**6 / n)
● Averaged SGD works best with large number of features.
● After PCA, multiply training data by c such that L2 norm will be
equals to 1.
19. Stochastic Gradient Descent (SGD)
Pros:
1. Fast
2. Ease of implementation
3. Sound theoretical results
Cons:
1. Hyperparameters tuning
2. Sensitive to feature scaling
3. Not multiclass
20. Multilabel and Multiclass classification
● Multiclass: classifying more than 2 classes. For
example, classifying digits.
● Multilabel: assigning a set of topics to each sample.
For example, assignment of topics to an article.
● Multioutput-multiclass: fixed number of output
variables, each of which can take on arbitrary number
of values. For example, predicting a fruit and its color,
where each fruit can take on arbitrary set of values
from {‘blue’, ‘orange’, ‘green’, ‘white’,...}.
24. One-vs-Rest (OVR)
Training: Fits one classifier per
class against all other data as a
negative class. In total K classifiers.
Prediction: applies K classifiers to a
new data point. Selects the one
that got a positive class. In case of
ties, selects the class with highest
confidence.
Pros:
● Efficient
● Interpretable
26. One-vs-One (OVO)
Training: Fits (K-1) classifier per
class against each other class. In
total K*(K-1)/2 classifiers.
Prediction: applies K*(K-1)/2
classifiers to a new data point.
Selects the class that got the
majority of votes (“+1”). In case of
ties, selects the class with highest
confidence.
Pros:
● Used for Kernel algorithms (e.g.
“SVM”).
Cons:
● Not as fast as OVR
27. Error-Correcting Output Codes (ECOC)
Training: 1) Obtain a binary
codeword for each class of
length c. 2) Learn a separate
binary classifier for each position
of a codeword. In total, c
classifiers.
Prediction: Apply c classifiers to
a new data point and select the
class closest to a datapoint by
Hamming distance.
28. Error-Correcting Output Codes (ECOC)
How to obtain codewords?
1) Row separation
2) Column separation
Pros:
● Can be more
correct than
OVR