This document discusses XGBoost, an optimized distributed gradient boosting library. It begins by explaining what problems XGBoost can solve like binary classification, regression, and ranking. It then discusses the key concepts in XGBoost including boosted trees, GBDT, tree ensembles, and additive training. XGBoost builds an ensemble of trees using gradient boosting and additive training to minimize loss. It provides efficient algorithms for split finding to construct trees level-by-level to maximize the loss drop at each step.
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
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Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
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Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
This is the slide from my talk at FULokoja Ingressive meetup.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured and structured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree-based algorithms are considered best-in-class right now. XGBoost model has the best combination of prediction performance and processing time compared to other algorithms.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
This is the slide from my talk at FULokoja Ingressive meetup.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured and structured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree-based algorithms are considered best-in-class right now. XGBoost model has the best combination of prediction performance and processing time compared to other algorithms.
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
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.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Methods of Optimization in Machine LearningKnoldus Inc.
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.
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.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
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.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
발표자: 이활석(NAVER)
발표일: 2017.11.
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨 지고 있습니다. 본 과정에서는 비지도학습의 가장 대표적인 방법인 오토인코더의 모든 것에 대해서 살펴보고자 합니다. 차원 축소관점에서 가장 많이 사용되는Autoencoder와 (AE) 그 변형 들인 Denoising AE, Contractive AE에 대해서 공부할 것이며, 데이터 생성 관점에서 최근 각광 받는 Variational AE와 (VAE) 그 변형 들인 Conditional VAE, Adversarial AE에 대해서 공부할 것입니다. 또한, 오토인코더의 다양한 활용 예시를 살펴봄으로써 현업과의 접점을 찾아보도록 노력할 것입니다.
1. Revisit Deep Neural Networks
2. Manifold Learning
3. Autoencoders
4. Variational Autoencoders
5. Applications
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Methods of Optimization in Machine LearningKnoldus Inc.
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.
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.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Algorithm evaluation using Item Response TheoryCSIRO
How do you evaluate a portfolio of algorithms? Suppose we have the results for a set of algorithms on a given set of problems. We can find which algorithm performs best for each problem and find the algorithm that performs best on the greatest number of problems. But, there is a limitation with this approach. We are only looking at the overall best! Suppose a certain algorithm gives the best performance on hard problems, but not on easy problems. We would miss this algorithm by using the “overall best” approach.
Item Response Theory (IRT) is used to design, analyse and score test questions/questionnaires that measure hidden qualities such as stress proneness, political inclinations, or verbal/mathematical ability. It is a methodology used in educational psychometrics. Participants take tests and IRT is used to determine the ability of participants and discrimination and difficulty of test questions. We use a novel mapping of the traditional IRT framework modified to the algorithm evaluation domain. Using this new mapping, we elicit a richer suite of characteristics including stability and anomalousness that describe important aspects of algorithm performance. We find the strengths and weaknesses of algorithms in the problem space. Using the algorithm strengths and weaknesses we construct a smaller portfolio of algorithms that gives good performance.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
2. 1. Introduction
2. Boosted Tree
3. Tree Ensemble
4. Additive Training
5. Split Algorithm
School of Computer Sicience and
3. 1 Introduction
• What Xgboost can do ?
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Binary
Classification
Multiclass
Classification
Regression Learning to
Rank
By 02. March.2017
Scalable, Portable and Distributed Gradient
Boosting (GBDT, GBRT or GBM) Library
Support Language
• Python
• R
• Java
• Scala
• C++ and more
Support Platform
• Runs on single machine,
• Hadoop
• Spark
• Flink
• DataFlow
4. 2 Boosted Tree
• Variants:
• GBDT: gradient boosted decision tree
• GBRT: gradient boosted regression tree
• MART: Multiple Additive Regression Trees
• LambdaMART, for ranking task
• ...
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5. 2.1 CART
• CART: Classification and Regression Tree
• Classification
• Three Classes
• Two Variables
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6. 2.1 CART
Prediction
• predicting price of 1993-model cars.
• standardized (zero mean,unit variance)
School of Computer Sicience andpartition
7. 2.1 CART
• Information Gain
• Gain Ratio
• Gini Index
• Pruning: prevent overfitting
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Which variable to use for division
8. 2.2 CART
• Input: Age, gender, occupation
• Goal: Does the person like computer games
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9. 3 Tree Ensemble
• What is Tree Ensemble ?
• Single Tree is not powerful enough
• Benifts of Tree Ensemble ?
• Very widely used
• Invariant to scaling of inputs
• Learn higher order interaction between features
• Scalable
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Boosted Tree
Random Forest
Tree
Ensemble
10. 3 Tree Ensemble
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Prediction of is sum of scores predicted by each of the tree
11. 3 Tree Ensemble-Elements of Supervised Learning
• Linear model
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Optimizing training loss encourages predictive models
Opyimizing regularization encourages simple models
12. 3 Tree Ensemble
• Assuming we have k trees
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• Parameters
• Including structure of each tree, and the score in the leaf
• Or simply use function as parameters
• Instead learning weights in R^d, we are learning functions ( trees)
13. 3 Tree Ensemble
• How can we learn functions?
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The height
in each
segment
Splitting
positions
• Training loss: How will the function fit on the points?
• Regularization: How do we define complexity of the function?
14. 3 Tree Ensemble
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Regularization
Number of splitting points
L2 norm of the leaf weights
Training loss:
error =
15. 3 Tree Ensemble
• We define tree by a vector of scores in leafs, and a leaf index mapping
function that maps an instance to a leaf
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16. 3 Tree Ensemble
• Objective:
• Definiation of Complexity
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17. 4 Addictive Training (Boosting)
• We can not use methods such as SGD, to find f ( since thet are trees,
instead of just numerical vectors)
• Start from constant prediction, add a new function each time.
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18. 4 Addictive Training (Boosting)
• How do we decide which f to add ?
• The prediction at round t is
• Consider square loss
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19. 4 Addictive Training (Boosting)
• Taylor expansion of the objective
• Objective after expansion
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20. 4 Addictive Training (Boosting)
• Our new goal, with constants removed
• Benifits
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21. 4 Addictive Training (Boosting)
• Define the instance set in leaf j as
• Regroup the objective by each leaf
• This is sum of T independent quadratic functions
• Two facts about single variable quadratic function
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22. 4 Addictive Training (Boosting)
• Let us define
• Results
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There can be infinite possible tree
structures
23. 4 Addictive Training (Boosting)
• Greedy Learning , we grow the tree greedily
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24. 5 Spliting algorithm
• Efficeint finding of the best split
• What is the gain of a split rule xj < a ? say xj is age
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All we need is sume of g and h in each side, and calculate
• Left to right linear scan over sorted instance is enough to decide the best split
28. References
• http://www.52cs.org/?p=429
• http://www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf
• http://www.sigkdd.org/node/362
• http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf
• http://www.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf
• https://github.com/dmlc/xgboost/blob/master/demo/README.md
• http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/
• http://xgboost.readthedocs.io/en/latest/model.html
• http://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-
learning/
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29. Suplementary
• Tree model, works very well on tabular data, easy to use,
and interpret and control
• It can not extrapolate
• Deep Forest: Towards An Alternative to Deep Neural
Networks, Zhi-Hua Zhou, Ji Feng, Nanjing University
• Submitted on 28 Feb 2017
• Comparable performance and easy to train (less parameters)
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XGBoost is one of the most frequently used package to win machine learning challenges
XGBoost can solve billion scale problems with few resources and is widely adopted in industry.
XGBoost is an optimized distributed gradient boosting system designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples. The most recent version integrates naturally with DataFlow frameworks(e.g. Flink and Spark)
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
Fitting well in training data at least get you close to training data
which is hopefully close to the underlying distribution
Simpler models tends to have smaller variance in future
predictions, making prediction stable
1. Almost half of data mining competition are won by using some variants of tree ensemble methods
2. so you do not need to do careful features normalization
3. and are used in Industry
