Xgboost: A Scalable Tree Boosting System - Explained
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Explaining the theoretical foundation, implementation details and experimental results from the original xgboost paper.
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XGBoost: the algorithm that wins every competition
meet.ml #1 - Applied Big Data and Machine Learning
Poznań Data Science Meetup
Poznań Univeristy of Technology; April 28th, 2016
Introduction to XGboost
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.
Introduction to XGBoost
This document provides an introduction to XGBoost, including:
1. XGBoost is an important machine learning library that is commonly used by winners of Kaggle competitions.
2. A quick example is shown using XGBoost to predict diabetes based on patient data, achieving good results with only 20 lines of simple code.
3. XGBoost works by creating an ensemble of decision trees through boosting, and focuses on explaining concepts at a high level rather than detailed algorithms.
XGBoost @ Fyber
Fyber implemented XGBoost models for two main use cases: Audience Vault Reach prediction and CTR prediction for their offer wall. For Audience Vault Reach, XGBoost with Spark was used to predict audience size over the next 14 days using historical user activity data. For CTR prediction, XGBoost ranked offers based on attributes to better estimate performance compared to old manual configurations. Both models involved data preprocessing, feature engineering, training XGBoost pipelines on Spark, and integrating the models into products.
Introduction of Xgboost
XGBoost is a machine learning algorithm based on boosting decision trees. It builds decision trees sequentially to optimize a specified loss function. At each step, it fits a decision tree to the residuals of the previous tree to minimize the loss. It uses regularization to control overfitting by penalizing complex trees with many leaves and high weights. To determine the best split at each node, it calculates the loss reduction from splitting the node versus keeping it whole. The split that maximizes loss reduction is selected.
Xgboost
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
XGBoost (System Overview)
The document discusses XGBoost, an effective and scalable gradient boosting system for machine learning. XGBoost has achieved success in many real-world applications and Kaggle competitions due to its regularized learning approach, sparsity awareness, and cache-aware design which allows it to process large datasets efficiently. The document outlines the history of boosting techniques, describes XGBoost's algorithmic features and system design, and evaluates its performance on several benchmark datasets.
XGBoost & LightGBM
This document summarizes gradient boosting algorithms XGBoost and LightGBM. It covers decision trees, overfitting, regularization, feature engineering, parameter tuning, evaluation metrics, and comparisons between XGBoost and LightGBM. Key aspects discussed include XGBoost and LightGBM's tolerance of outliers, non-standardized features, collinear features, and NaN values. Parameter tuning, using RandomizedSearchCV and GridSearchCV, and ensembling models to optimize multiple metrics are also covered.
Recommended
XGBoost: the algorithm that wins every competition
meet.ml #1 - Applied Big Data and Machine Learning
Poznań Data Science Meetup
Poznań Univeristy of Technology; April 28th, 2016
Introduction to XGboost
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.
Introduction to XGBoost
This document provides an introduction to XGBoost, including:
1. XGBoost is an important machine learning library that is commonly used by winners of Kaggle competitions.
2. A quick example is shown using XGBoost to predict diabetes based on patient data, achieving good results with only 20 lines of simple code.
3. XGBoost works by creating an ensemble of decision trees through boosting, and focuses on explaining concepts at a high level rather than detailed algorithms.
XGBoost @ Fyber
Fyber implemented XGBoost models for two main use cases: Audience Vault Reach prediction and CTR prediction for their offer wall. For Audience Vault Reach, XGBoost with Spark was used to predict audience size over the next 14 days using historical user activity data. For CTR prediction, XGBoost ranked offers based on attributes to better estimate performance compared to old manual configurations. Both models involved data preprocessing, feature engineering, training XGBoost pipelines on Spark, and integrating the models into products.
Introduction of Xgboost
XGBoost is a machine learning algorithm based on boosting decision trees. It builds decision trees sequentially to optimize a specified loss function. At each step, it fits a decision tree to the residuals of the previous tree to minimize the loss. It uses regularization to control overfitting by penalizing complex trees with many leaves and high weights. To determine the best split at each node, it calculates the loss reduction from splitting the node versus keeping it whole. The split that maximizes loss reduction is selected.
Xgboost
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
XGBoost (System Overview)
The document discusses XGBoost, an effective and scalable gradient boosting system for machine learning. XGBoost has achieved success in many real-world applications and Kaggle competitions due to its regularized learning approach, sparsity awareness, and cache-aware design which allows it to process large datasets efficiently. The document outlines the history of boosting techniques, describes XGBoost's algorithmic features and system design, and evaluates its performance on several benchmark datasets.
XGBoost & LightGBM
This document summarizes gradient boosting algorithms XGBoost and LightGBM. It covers decision trees, overfitting, regularization, feature engineering, parameter tuning, evaluation metrics, and comparisons between XGBoost and LightGBM. Key aspects discussed include XGBoost and LightGBM's tolerance of outliers, non-standardized features, collinear features, and NaN values. Parameter tuning, using RandomizedSearchCV and GridSearchCV, and ensembling models to optimize multiple metrics are also covered.
Demystifying 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.
Xgboost
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
Feature Engineering - Getting most out of data for predictive models
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Winning data science competitions, presented by Owen 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/
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This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.
Kaggleのテクニック
Kaggleのあるコンテンストの優勝者が作ったモデルをケーススタディとして、kaggleテクニックを解説します。特にStackingを中心に解説しています。CNNにはあえて触れませんでした。また、t-SNEやxgboostの概略にも触れました。
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Learning System Model.
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Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
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THANK YOU
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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.
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As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
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2. Analyse the importance of hyperparameters.
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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.
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For companies that solve real-world problems and generate revenue from the data science products, being able to understand why a model makes a certain prediction can be as crucial as achieving high prediction accuracy in many applications. However, as data scientists pursuing higher accuracy by implementing complex algorithms such as ensemble or deep learning models, the algorithm itself becomes a blackbox and it creates the trade-off between accuracy and interpretability of a model’s output.
To address this problem, a unified framework SHAP (SHapley Additive exPlanations) was developed to help users interpret the predictions of complex models. In this session, we will talk about how to apply SHAP to various modeling approaches (GLM, XGBoost, CNN) to explain how each feature contributes and extract intuitive insights from a particular prediction. This talk is intended to introduce the concept of general purpose model explainer, as well as help practitioners understand SHAP and its applications.
Boosting - An Ensemble Machine Learning Method
This document compares and contrasts boosting with other ensemble methods such as bagging and random forests. It discusses two specific boosting algorithms - AdaBoost, which fits models on weighted labels, and gradient boosting, which fits models on residuals from previous models. Both aim to produce low bias, low variance predictions by building models sequentially. The document provides pseudocode for AdaBoost classification and gradient boosting regression, and explains how boosting methods work to improve upon previous predictions at each step of the ensemble.
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There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
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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.
Xgboost
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
Feature Engineering - Getting most out of data for predictive models
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Winning data science competitions, presented by Owen 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/
decision tree regression
This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.
Kaggleのテクニック
Kaggleのあるコンテンストの優勝者が作ったモデルをケーススタディとして、kaggleテクニックを解説します。特にStackingを中心に解説しています。CNNにはあえて触れませんでした。また、t-SNEやxgboostの概略にも触れました。
科学と機械学習のあいだ：変量の設計・変換・選択・交互作用・線形性
機械学習はそもそも汎用的な枠組みであり基本的には対象分野に依らず利活用できる。従って、分野を限定して物質科学に機械学習を活用するなどと言う場合、その本質的難所の大部分は、有効な変量の設計・変換・選択・交互作用・線形性などに関する、いわゆるfeature engineeringの問題に帰する。特に、科学研究では端的な説明因子や共通パターンの探究こそが目的であることが多く、その場しのぎではない方法論が期待されている。本発表では、医薬品、機能制御剤、有機EL材料、食品、化粧品、と波及範囲が広い有機低分子について、その物性の予測と生物活性の予測の違いを例に、関わってきた問題・方法・課題について紹介する。
http://ibisml.org/ibis2016/session3/
実践多クラス分類 Kaggle Ottoから学んだこと
Kaggleのコンペに参加することで 色々な実践的ノウハウを学んだので そのノウハウを共有する
p.3～53 コンペ中に自分がやったこと p.54～99 ハイランカーがやっていたこと p.100～ ハイランカーかやっていたことを 自分も実際にやってみる
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Tips & Tricks for Feature Engineering / Applied Machine Learning. This presentation was given as part of the Sao Paulo ML meetup.
Automated Machine Learning
The document discusses automated machine learning (AutoML). It defines AutoML as providing methods to make machine learning more efficient and accessible to non-machine learning experts. AutoML aims to automate tasks like data preprocessing, feature engineering, algorithm selection and hyperparameter optimization. This can reduce costs, increase productivity for data scientists and democratize machine learning. The document also lists several AutoML tools that provide hyperparameter tuning, full pipeline optimization or neural architecture search.
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)
This document provides an overview of machine learning classification and decision trees. It discusses key concepts like supervised vs. unsupervised learning, and how decision trees work by recursively partitioning data into nodes. Random forest and gradient boosted trees are introduced as ensemble methods that combine multiple decision trees. Random forest grows trees independently in parallel while gradient boosted trees grow sequentially by minimizing error from previous trees. While both benefit from ensembling, gradient boosted trees are more prone to overfitting and random forests are better at generalizing to new data.
Machine Learning ppt
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Overview of tree algorithms from decision tree to xgboost
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.
Lecture6 - C4.5
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.
Automatic Machine Learning, AutoML
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
AutoML lectures (ACDL 2019)
Automated machine learning lectures given at the Advanced Course on Data Science & Machine Learning. AutoML, hyperparameter optimization, Bayesian optimization, Neural Architecture Search, Meta-learning, MAML
Understanding Bagging and Boosting
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.
Unified Approach to Interpret Machine Learning Model: SHAP + LIME
For companies that solve real-world problems and generate revenue from the data science products, being able to understand why a model makes a certain prediction can be as crucial as achieving high prediction accuracy in many applications. However, as data scientists pursuing higher accuracy by implementing complex algorithms such as ensemble or deep learning models, the algorithm itself becomes a blackbox and it creates the trade-off between accuracy and interpretability of a model’s output.
To address this problem, a unified framework SHAP (SHapley Additive exPlanations) was developed to help users interpret the predictions of complex models. In this session, we will talk about how to apply SHAP to various modeling approaches (GLM, XGBoost, CNN) to explain how each feature contributes and extract intuitive insights from a particular prediction. This talk is intended to introduce the concept of general purpose model explainer, as well as help practitioners understand SHAP and its applications.
Boosting - An Ensemble Machine Learning Method
This document compares and contrasts boosting with other ensemble methods such as bagging and random forests. It discusses two specific boosting algorithms - AdaBoost, which fits models on weighted labels, and gradient boosting, which fits models on residuals from previous models. Both aim to produce low bias, low variance predictions by building models sequentially. The document provides pseudocode for AdaBoost classification and gradient boosting regression, and explains how boosting methods work to improve upon previous predictions at each step of the ensemble.
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Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
Data analysis01 singlevariable
The document discusses analyzing single variable data through shape, distribution, and outliers. It emphasizes keeping analysis simple using techniques like histograms and kernel density estimates over complex solutions. Histograms can lose information and have ambiguous bin widths and placements, while kernel density estimates provide continuous, smoother representations of the data. Understanding the problem domain and having clear goals is important before analyzing data to gain useful insights.
Optimising Xapian
Talk given to UKUUG on 9th August 2009 about the Xapian search engine, and some of the experiences I've had trying to optimise its design and implementation.
Chap8 basic cluster_analysis
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Data Mining In Market Research
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Data Science - Part V - Decision Trees & Random Forests
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Module III - Classification Decision tree (1).pptx
Decision trees utilize a tree structure to model relationships between features and outcomes. They work by recursively splitting the data into increasingly homogeneous subsets based on feature values, represented as branches in the tree. The C5.0 algorithm is an improved version of earlier algorithms and is widely used due to its strong out-of-the-box performance. It automatically learns the optimal structure of the tree and prunes branches to avoid overfitting, resulting in an accurate and interpretable model.
Performance By Design
The document discusses designing database architectures and applications for high performance. It provides guidance on defining clear performance requirements, designing databases and applications to meet those requirements, and techniques like indexing, partitioning, caching, and array processing to optimize performance. The goal is to map performance needs to the architecture from the start to avoid later issues and ensure requirements are actually achieved.
Ml7 bagging
Three sentences:
Bagging creates multiple decision trees from bootstrap samples of the data, aggregates the results to reduce variance. It grows trees independently, while random forest decorrelates trees by using random subsets of features. Extra trees introduces even more randomness by selecting features and splits randomly rather than greedily.
2 extreme performance - smaller is better
This document discusses various data compression methods that can be used to reduce the size of databases and improve performance. It covers physical compression techniques like data compression, archiving, and using smaller data types. It also covers logical compression methods like partitioning data horizontally and vertically, creating covering indexes, and filtered indexes. The goal of these compression methods is to address challenges of storing and accessing large amounts of data by minimizing the data size and amount of data that needs to be processed for queries and other operations.
Performance By Design
This document discusses principles for designing systems and databases for high performance. It emphasizes that:
1. Performance requirements should be specified upfront and the architecture should support meeting those requirements.
2. The database design impacts performance, so logical and physical designs like normalization, indexing, partitioning, and caching should be considered.
3. Application architecture also affects performance through techniques like minimizing network traffic and SQL parsing overhead.
Bank loan purchase modeling
Bank - Loan Purchase Modeling
This case is about a bank which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget. The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign. The dataset has data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.
Our job is to build the best model which can classify the right customers who have a higher probability of purchasing the loan. We are expected to do the following:
EDA of the data available. Showcase the results using appropriate graphs
Apply appropriate clustering on the data and interpret the output .
Build appropriate models on both the test and train data (CART & Random Forest). Interpret all the model outputs and do the necessary modifications wherever eligible (such as pruning).
Check the performance of all the models that you have built (test and train). Use all the model performance measures you have learned so far. Share your remarks on which model performs the best.
Cs437 lecture 1-6
This document provides an overview and schedule for a course on Data Warehousing and Mining. The course will cover topics like data warehousing, data cubes, OLAP, data normalization and de-normalization, and various data mining techniques. A tentative schedule is provided that includes lectures on introduction, data warehousing motivation, indexing, building warehouses, mining techniques like regression, clustering, decision trees. Textbook references and grading plan are also outlined.
Database optimization
optimize you database with some tips
most of indexes and partitioning types are for oracle database
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The document provides an introduction and overview of CART (Classification and Regression Trees) machine learning algorithm. It discusses how CART gained recognition over time, from its introduction in 1984 to widespread use today due to advances in data mining. It summarizes the key steps of building a CART model, including binary splitting, competitors and surrogates, and interpreting the resulting decision tree to extract rules.
Introduction to RandomForests 2004
This document provides an introduction to random forests, which are an ensemble machine learning method for classification and regression. Random forests build on decision trees but average multiple tree predictions to improve accuracy over a single tree. Each tree is constructed using a random sample of data and random subsets of features. This introduces variability that improves predictive performance compared to single trees or bagged trees that use all features. The document outlines the key characteristics and advantages of random forests, such as high accuracy, ability to handle large datasets with many variables, and resistance to overfitting.
[Women in Data Science Meetup ATX] Decision Trees
Decision trees are a supervised learning technique that can be used for both classification and regression problems. They work by recursively splitting a data set into purer and purer subsets based on an impurity measure, with the goal of ending up with subsets consisting of single class members. Common impurity measures include information gain and the GINI index. Decision trees can overfit data, so techniques like bagging and random forests are used to combine multiple decision trees to reduce variance.
decisiontrees (3).ppt
This document provides an introduction to decision trees, which are a type of predictive model that uses a tree-like structure to determine the class of records. Decision trees work by recursively splitting a dataset into purer subsets based on attribute values, resulting in a flowchart-like structure. They have an intuitive appeal as rules can be represented visually or as "if-then" statements. The document discusses key aspects of decision trees such as how they are constructed, evaluated, pruned to prevent overfitting, and their advantages and limitations for classification tasks.
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Machine Learning Unit-5 Decesion Trees & Random Forest.pdf
Data Science - Part V - Decision Trees & Random Forests
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HyperLogLog and friends
This talk is a quick introduction to counting sketches and HyperLogLog (HLL) in particular. HLL is a probabilistic data structure that can be used for counting the number of distinct elements (cardinality) in sub-linear space. With just 2 KB memory footprint it can approximate count for millions of distinct items with an error below 2%. This has a range of applications in batch, stream, and distributed processing, most importantly reducing the amount of data we have to store or transmit over the wire, but also several pitfalls, for example when it comes to computing an intersection between two sets. In this talk, I will explain the main idea and some of the applications, show code and benchmark examples from my previous work, and provide further references for those who want to learn more.
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Cxense helps companies understand their audience and build great online experiences. Cxense Insight and DMP let customers annotate, filter, segment and target their users based on the consumed content and performed actions in real-time. With more than 5000 active websites, Insight alone tracks more than a billion unique users with more than 15 billions page views per month. To leverage the huge amounts of data in real-time, we have built a large distributed system relying on techniques familiar from databases, information retrieval and data mining. In this talk, we outline our solutions and give some insight into the technology we use and the challenges we face. This introduction should be interesting to undergraduate and PhD students as well as experienced researchers and engineers.
Efficient Query Processing in Web Search Engines
An introduction to efficient query processing in Web search engines. Guest lecture in INF3800 at the University of Oslo.
Leveraging Big Data and Real-Time Analytics at Cxense
Abstract: Cxense Insight helps companies to understand their audience and build great online experiences. Our interactive UI and APIs help customers to
annotate, filter, segment and target their users based on the visited content and actions in realtime. Today we already track more than half a billion of unique user identities across more than 5000 web-sites, contributing to more than 10 billions of analytics events on a monthly basis.
To leverage these amounts of data in realtime, we built a large distributed system relying on the concepts familiar from databases, information retrieval and data mining. The first part of this talk will therefore give an insight into the challenges, the architecture and the techniques we have used. While the second part of the talk will briefly demonstrate our UI and APIs in action. We hope that both parts will be interesting for undergraduate students taking IR/DB courses as well as PhD students, experienced researchers and staff.
Yet another intro to Apache Spark
Spark is a framework for efficient parallel data processing. It uses resilient distributed datasets (RDDs) that can be operated on in parallel, cached in memory, and recomputed when needed. The core of Spark provides functions for data sharing and basic operations like filtering, mapping, and reducing RDDs. Additional Spark modules provide capabilities for SQL, streaming, machine learning, and graph processing.
Efficient Query Processing in Distributed Search Engines
This document outlines Simon Jonassen's research on efficient query processing in distributed search engines. It discusses three main areas:
1) Partitioned query processing, including semi-pipelined and pipelined approaches with skipping to improve throughput and latency.
2) Skipping and pruning techniques like efficient compression and linear programming to improve pruning for disjunctive queries.
3) Caching approaches including modeling static two-level caching and prefetching query results to improve search engine performance. The research is evaluated using large test collections and clusters of up to 9 nodes.
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Xgboost: A Scalable Tree Boosting System - Explained
- 1. XGBoost: A Scalable Tree Boosting System Simon Lia-Jonassen
- 2. Motivation Used by majority of winning solutions on Kaggle, 2nd most popular method after DNN. Also used by 10 best teams in KDDCup’15. Applies to classification, regression and learning-to-rank tasks. Usually outperforms alternatives in an out-of-the-box setting. Combines a good theoretical foundation and a highly efficient implementation. So, how does it work?
- 3. Decision Tree Boosting Number of trees Tree function, maps to a set of leaf weights Instance features
- 4. Regularized Learning Objective Prediction loss Complexity penalty Number of leaves L2 regularization on leaves weights
- 5. Regularized Learning Objective First order gradient of the loss function Second order gradient of the loss function By additive definition Where: However, for example:
- 6. Regularized Learning Objective By expansion: For each instance For each leaf For each instance in the leaf
- 7. Regularized Learning Objective Optimal leaf weight for a fixed structure: By substitution:
- 8. Gradient Tree Boosting Before we split Left split Right split Split penalty
- 10. Optimizations Shrinkage More trees Column subsampling Prevents over-fitting Approximate split finding Faster AUC convergence Sparsity-aware split finding Visit only non-missing values Cache-aware parallel column block access Fewer misses on large datasets Block compression and sharding Faster I/O for out-of-core computation
- 11. Optimizations Shrinkage More trees Column subsampling Prevents over-fitting Approximate split finding Faster AUC convergence Sparsity-aware split finding Visit only non-missing values Cache-aware parallel column block access Fewer misses on large datasets Block compression and sharding Faster I/O for out-of-core computation
- 12. Optimizations Shrinkage More trees Column subsampling Prevents over-fitting Approximate split finding Faster AUC convergence Sparsity-aware split finding Visit only non-missing values Cache-aware parallel column block access Fewer misses on large datasets Block compression and sharding Faster I/O for out-of-core computation
- 13. Optimizations Shrinkage More trees Column subsampling Prevents over-fitting Approximate split finding Faster AUC convergence Sparsity-aware split finding Visit only non-missing values Cache-aware parallel column block access Fewer misses on large datasets Block compression and sharding Faster I/O for out-of-core computation
- 14. Optimizations Shrinkage More trees Column subsampling Prevents over-fitting Approximate split finding Faster AUC convergence Sparsity-aware split finding Visit only non-missing values Cache-aware parallel column block access Fewer misses on large datasets Block compression and sharding Faster I/O for out-of-core computation
- 15. Optimizations
- 16. Further reading The paper: https://arxiv.org/pdf/1603.02754.pdf XGBoost tutorial: http://xgboost.readthedocs.io/en/latest/model.html A great deck of slides: https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf A simple usage example: https://www.kaggle.com/kevalm/xgboost-implementation-on-iris-dataset-python DataCamp mini-course: https://campus.datacamp.com/courses/extreme-gradient-boosting-with-xgboost