Boosting techniques like AdaBoost combine the predictions of many weak learner models to create a stronger joint model. AdaBoost uses stumps, or decision trees with one node and two leaves, as the weak learners. It adjusts the weights of samples to focus on incorrectly classified samples. Over many iterations, it boosts the weights of harder to classify samples to improve predictive performance compared to a single weak learner.
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.
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.
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.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you 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.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
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.
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.
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.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you 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.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
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.
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
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.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
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.
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
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.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntEugene Yan Ziyou
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Machine learning in science and industry — day 2arogozhnikov
- decision trees
- random forest
- Boosting: adaboost
- reweighting with boosting
- gradient boosting
- learning to rank with gradient boosting
- multiclass classification
- trigger in LHCb
- boosting to uniformity and flatness loss
- particle identification
Graph Methods for Generating Test Cases with Universal and Existential Constr...Sylvain Hallé
We introduce a generalization of the t -way test case generation problem, where parameter t is replaced by a set Φ of Boolean conditions on attribute values. We then present two reductions of this problem to graphs; first, to graph colouring, where we link the the minimal number of tests to the chromatic number of some graph; second, to hypergraph vertex covering. This latter formalization allows us to handle problems with constraints of two kinds: those that must be true for every generated test case, and those that must be true for at least one test case. Experimental results show that the proposed solution produces test suites of slightly smaller sizes than a range of existing tools, while being more general: to the best of our knowledge, our work is the first to allow existential constraints over test cases.
This worksheet contains three simple exercises for you to work through in your own time to consolidate your understanding of normal stress. It is associated with these three NinetyEast tutorials:
- http://www.ninetyeast.net/physics/university/mechanics-of-materials/what-is-stress/what-is-normal-stress
- http://www.ninetyeast.net/physics/university/mechanics-of-materials/what-is-stress/what-is-normal-stress/normal-stress-examples
- http://www.ninetyeast.net/physics/university/mechanics-of-materials/what-is-stress/what-is-normal-stress/normal-stress-example-2-chair
Here a Review of the Combination of Machine Learning models from Bayesian Averaging, Committees to Boosting... Specifically An statistical analysis of Boosting is done
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Similar to bagging, boosting technique also combine the predictions of
many models and (weak learners) to form a better model (string
learner)
AdaBoost
AdaBoost stands for Adaptive Boosting
The model adjusts the weights of weak learners and try to make the
model better.
Stumps (Base Learner)
A tree with 1 node and 2 leaves is called a stump.
Stump are comparatively weak learners as its using only 1 feature.
First stump will be created based on entropy/gini value.
Errors/accuracy of the stumps can vary.
AdaBoost will be dealing with ‘n’ number of stumps and will provide a
stringer model.
4. Sample weight
1/5
1/5
1/5
1/5
1/5
Row No. x1 x2 x3 y
1 1 Yes
2 0 Yes
3 0 No
4 0 No
5 1 Yes
New weight for incorrect sample
New weight for correct sample
New Weight = Sample Weight * e^(Performance) = (1/5)* e^(0.69) = 0.398
New Weight = Sample Weight * e^(-Performance) = (1/5)* e^(-0.69) = 0.1
Updated weight
0.100
0.398
0.100
0.100
0.100
Normalized weight
0.125
0.499
0.125
0.125
0.125
Based on normalized weights, the samples will be grouped into buckets.
Adaboost will generate random numbers
Based on the random numbers getting created, the samples will be added to a new dataset.
New dataset will make same size of original. In the samples can be repeated. Using this data
the process will be repeated.
Considering the amount of weight first stump made second tree can do a better prediction.
Prediction will stand with the class where sum of performance of stumps is higher.
Bucket
0 to 0.125
0.125 to 0.624
0.624 to 0.749
0.749 to 0.875
0.875 to 1
