This document discusses accelerating the random forest algorithm for parallel hardware. It provides an overview of random forests and their implementation. The key points are:
1) Random forests build many decision trees on randomly sampled data and aggregate results, and can be parallelized by building trees simultaneously.
2) The implementation pre-orders data by predictor and "restages" data at each node to maintain locality during training. This allows highly regular processing.
3) Initial tests show speedups over existing R packages, especially for larger datasets and regression problems. Further optimization is needed for large-cardinality categorical predictors.
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
This document discusses decision trees and random forests for classification problems. It explains that decision trees use a top-down approach to split a training dataset based on attribute values to build a model for classification. Random forests improve upon decision trees by growing many de-correlated trees on randomly sampled subsets of data and features, then aggregating their predictions, which helps avoid overfitting. The document provides examples of using decision trees to classify wine preferences, sports preferences, and weather conditions for sport activities based on attribute values.
Random Forest Classifier in Machine Learning | Palin AnalyticsPalin analytics
Random Forest is a supervised learning ensemble algorithm. Ensemble algorithms are those which combine more than one algorithms of same or different kind for classifying objects....
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
Decision Trees for Classification: A Machine Learning AlgorithmPalin analytics
Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covariates or for developing prediction algorithms for a target variable.
Introduction to random forest and gradient boosting methods a lectureShreyas S K
This presentation is an attempt to explain random forest and gradient boosting methods in layman terms with many real life examples related to the concepts
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
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
- - - - - - -
The document discusses random forest, an ensemble classifier that uses multiple decision tree models. It describes how random forest works by growing trees using randomly selected subsets of features and samples, then combining the results. The key advantages are better accuracy compared to a single decision tree, and no need for parameter tuning. Random forest can be used for classification and regression tasks.
This document discusses decision trees and random forests for classification problems. It explains that decision trees use a top-down approach to split a training dataset based on attribute values to build a model for classification. Random forests improve upon decision trees by growing many de-correlated trees on randomly sampled subsets of data and features, then aggregating their predictions, which helps avoid overfitting. The document provides examples of using decision trees to classify wine preferences, sports preferences, and weather conditions for sport activities based on attribute values.
Random Forest Classifier in Machine Learning | Palin AnalyticsPalin analytics
Random Forest is a supervised learning ensemble algorithm. Ensemble algorithms are those which combine more than one algorithms of same or different kind for classifying objects....
The document discusses decision trees and random forest algorithms. It begins with an outline and defines the problem as determining target attribute values for new examples given a training data set. It then explains key requirements like discrete classes and sufficient data. The document goes on to describe the principles of decision trees, including entropy and information gain as criteria for splitting nodes. Random forests are introduced as consisting of multiple decision trees to help reduce variance. The summary concludes by noting out-of-bag error rate can estimate classification error as trees are added.
Decision Trees for Classification: A Machine Learning AlgorithmPalin analytics
Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covariates or for developing prediction algorithms for a target variable.
Introduction to random forest and gradient boosting methods a lectureShreyas S K
This presentation is an attempt to explain random forest and gradient boosting methods in layman terms with many real life examples related to the concepts
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
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
- - - - - - -
The document discusses consistency of random forests. It summarizes recent theoretical results showing that random forests are consistent estimators under certain conditions. Specifically, it is shown that random forests are consistent if the number of features sampled at each node (mtry) increases with sample size and the minimum node size decreases with sample size. The document also discusses how consistency holds even when the splitting criteria are randomized, as in random forests, as long as the base classifiers are consistent.
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
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.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
Introduction to Some Tree based Learning MethodHonglin Yu
Random Forest, Boosted Trees and other ensemble learning methods build multiple models to improve predictive performance over single models. They combine "weak learners" like decision trees into a "strong learner". Random Forest adds randomness by selecting a random subset of features at each split. Boosting trains trees sequentially on weighted data from previous trees. Both reduce variance compared to bagging. Random Forest often outperforms Boosting while being faster to train. Neural networks can also be viewed as an ensemble method by combining simple units.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This is the most simplest and easy to understand ppt. Here you can define what is decision tree,information gain,gini impurity,steps for making decision tree there pros and cons etc which will helps you to easy understand and represent it.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
Random forest is an ensemble classifier that consists of many decision trees, where each tree depends on the values of a random vector sampled independently from the input data. It combines Breiman's "bagging" idea and the random selection of features to construct a set of decision trees with controlled variance. The random forest algorithm builds decision trees using randomly selected subsets of the training data and randomly selected subsets of input features. Each tree provides a class prediction and the class with the most votes becomes the random forest's prediction. Random forests have advantages including high accuracy, efficiency on large datasets, ability to handle thousands of variables, and estimates of feature importance.
Understanding the Machine Learning AlgorithmsRupak Roy
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
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.
1) The 1R algorithm generates a one-level decision tree by considering each attribute individually and assigning the majority class to each branch. It chooses the attribute with the minimum classification error.
2) Naive Bayes classification assumes attributes are independent and calculates the probability of each class using Bayes' rule. It handles missing and numeric attributes.
3) Decision tree algorithms like ID3 use a divide-and-conquer approach, recursively splitting the data on attributes that maximize information gain or gain ratio at each node.
4) Rule-based algorithms like PRISM generate rules to cover instances of each class sequentially, maximizing the ratio of correctly covered to total covered instances at each step.
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
- Measuring error rates on separate test and training sets to avoid overfitting
- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
RapidMiner offers many machine learning algorithms including support vector machines, decision trees, rule learners, lazy learners, Bayesian learners, and logistic regression. It also supports association rule mining and clustering. Specific algorithms include decision trees similar to C4.5, neural networks using backpropagation, and Bayesian Boosting which trains an ensemble of classifiers. RapidMiner also provides techniques for preprocessing data like feature selection, discretization, normalization, and sampling as well as validation and genetic algorithms for feature selection.
Machine Learning Decision Tree AlgorithmsRupak Roy
Details discussion about the Tree Algorithms like Gini, Information Gain, Chi-square for categorical and Reduction in variance for continuous variable. Let me know if anything is required. Happy to help. Enjoy machine learning! #bobrupakroy
This document discusses data reduction strategies for reducing large datasets. It describes data cube aggregation, which aggregates data into a simpler form by combining and summarizing data tables. Attribute subset selection is also covered, which reduces a large number of attributes by eliminating irrelevant attributes. The document provides an example of attribute subset selection using forward selection, backward elimination, and decision tree induction to select the most important attributes of age and gender from a dataset containing name, age, gender, address, and phone number attributes. Data reduction maintains data integrity while reducing volume and improving data mining efficiency on large datasets.
This document provides an overview of major data mining algorithms, including supervised learning techniques like decision trees, random forests, support vector machines, naive Bayes, and logistic regression. Unsupervised techniques discussed include clustering algorithms like k-means and EM, as well as association rule learning using the Apriori algorithm. Application areas and advantages/disadvantages of each technique are described. Libraries for implementing these algorithms in Python and R are also listed.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
Algoritma Random Forest beserta aplikasi nyabatubao
Random forest is an ensemble classifier that consists of many decision trees. It outputs the class that is the mode of the classes from individual trees. Each tree is constructed by selecting a random sample of training cases and a small random subset of input variables. Trees are fully grown and not pruned, and each tree votes for the most popular class. The random forest algorithm averages these votes for classification or averages predictions for regression. Random forests have advantages such as high accuracy, efficiency with large datasets, and estimates of variable importance.
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET Journal
This document evaluates the performance of various classification algorithms (logistic regression, K-nearest neighbors, decision tree, random forest, support vector machine, naive Bayes) on a heart disease dataset. It provides details on each algorithm and evaluates their performance based on metrics like confusion matrix, precision, recall, F1-score and accuracy. The results show that naive Bayes had the best performance in correctly classifying samples with an accuracy of 80.21%, while SVM had the worst at 46.15%. In general, random forest and naive Bayes performed best according to the evaluation.
The document discusses consistency of random forests. It summarizes recent theoretical results showing that random forests are consistent estimators under certain conditions. Specifically, it is shown that random forests are consistent if the number of features sampled at each node (mtry) increases with sample size and the minimum node size decreases with sample size. The document also discusses how consistency holds even when the splitting criteria are randomized, as in random forests, as long as the base classifiers are consistent.
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
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.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
Introduction to Some Tree based Learning MethodHonglin Yu
Random Forest, Boosted Trees and other ensemble learning methods build multiple models to improve predictive performance over single models. They combine "weak learners" like decision trees into a "strong learner". Random Forest adds randomness by selecting a random subset of features at each split. Boosting trains trees sequentially on weighted data from previous trees. Both reduce variance compared to bagging. Random Forest often outperforms Boosting while being faster to train. Neural networks can also be viewed as an ensemble method by combining simple units.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This is the most simplest and easy to understand ppt. Here you can define what is decision tree,information gain,gini impurity,steps for making decision tree there pros and cons etc which will helps you to easy understand and represent it.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
No machine learning algorithm dominates in every domain, but random forests are usually tough to beat by much. And they have some advantages compared to other models. No much input preparation needed, implicit feature selection, fast to train, and ability to visualize the model. While it is easy to get started with random forests, a good understanding of the model is key to get the most of them.
This talk will cover decision trees from theory, to their implementation in scikit-learn. An overview of ensemble methods and bagging will follow, to end up explaining and implementing random forests and see how they compare to other state-of-the-art models.
The talk will have a very practical approach, using examples and real cases to illustrate how to use both decision trees and random forests.
We will see how the simplicity of decision trees, is a key advantage compared to other methods. Unlike black-box methods, or methods tough to represent in multivariate cases, decision trees can easily be visualized, analyzed, and debugged, until we see that our model is behaving as expected. This exercise can increase our understanding of the data and the problem, while making our model perform in the best possible way.
Random Forests can randomize and ensemble decision trees to increase its predictive power, while keeping most of their properties.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Understanding and debugging decision trees
* Ensemble methods
* Bagging
* Random Forests
* When decision trees and random forests should be used?
* Python implementation with scikit-learn
* Analysis of performance
Random forest is an ensemble classifier that consists of many decision trees, where each tree depends on the values of a random vector sampled independently from the input data. It combines Breiman's "bagging" idea and the random selection of features to construct a set of decision trees with controlled variance. The random forest algorithm builds decision trees using randomly selected subsets of the training data and randomly selected subsets of input features. Each tree provides a class prediction and the class with the most votes becomes the random forest's prediction. Random forests have advantages including high accuracy, efficiency on large datasets, ability to handle thousands of variables, and estimates of feature importance.
Understanding the Machine Learning AlgorithmsRupak Roy
includes distinguishable definitions from supervised vs unsupervised learning with their types and the workflow, algorithm map;
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
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.
1) The 1R algorithm generates a one-level decision tree by considering each attribute individually and assigning the majority class to each branch. It chooses the attribute with the minimum classification error.
2) Naive Bayes classification assumes attributes are independent and calculates the probability of each class using Bayes' rule. It handles missing and numeric attributes.
3) Decision tree algorithms like ID3 use a divide-and-conquer approach, recursively splitting the data on attributes that maximize information gain or gain ratio at each node.
4) Rule-based algorithms like PRISM generate rules to cover instances of each class sequentially, maximizing the ratio of correctly covered to total covered instances at each step.
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
- Measuring error rates on separate test and training sets to avoid overfitting
- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
RapidMiner offers many machine learning algorithms including support vector machines, decision trees, rule learners, lazy learners, Bayesian learners, and logistic regression. It also supports association rule mining and clustering. Specific algorithms include decision trees similar to C4.5, neural networks using backpropagation, and Bayesian Boosting which trains an ensemble of classifiers. RapidMiner also provides techniques for preprocessing data like feature selection, discretization, normalization, and sampling as well as validation and genetic algorithms for feature selection.
Machine Learning Decision Tree AlgorithmsRupak Roy
Details discussion about the Tree Algorithms like Gini, Information Gain, Chi-square for categorical and Reduction in variance for continuous variable. Let me know if anything is required. Happy to help. Enjoy machine learning! #bobrupakroy
This document discusses data reduction strategies for reducing large datasets. It describes data cube aggregation, which aggregates data into a simpler form by combining and summarizing data tables. Attribute subset selection is also covered, which reduces a large number of attributes by eliminating irrelevant attributes. The document provides an example of attribute subset selection using forward selection, backward elimination, and decision tree induction to select the most important attributes of age and gender from a dataset containing name, age, gender, address, and phone number attributes. Data reduction maintains data integrity while reducing volume and improving data mining efficiency on large datasets.
This document provides an overview of major data mining algorithms, including supervised learning techniques like decision trees, random forests, support vector machines, naive Bayes, and logistic regression. Unsupervised techniques discussed include clustering algorithms like k-means and EM, as well as association rule learning using the Apriori algorithm. Application areas and advantages/disadvantages of each technique are described. Libraries for implementing these algorithms in Python and R are also listed.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
Algoritma Random Forest beserta aplikasi nyabatubao
Random forest is an ensemble classifier that consists of many decision trees. It outputs the class that is the mode of the classes from individual trees. Each tree is constructed by selecting a random sample of training cases and a small random subset of input variables. Trees are fully grown and not pruned, and each tree votes for the most popular class. The random forest algorithm averages these votes for classification or averages predictions for regression. Random forests have advantages such as high accuracy, efficiency with large datasets, and estimates of variable importance.
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET Journal
This document evaluates the performance of various classification algorithms (logistic regression, K-nearest neighbors, decision tree, random forest, support vector machine, naive Bayes) on a heart disease dataset. It provides details on each algorithm and evaluates their performance based on metrics like confusion matrix, precision, recall, F1-score and accuracy. The results show that naive Bayes had the best performance in correctly classifying samples with an accuracy of 80.21%, while SVM had the worst at 46.15%. In general, random forest and naive Bayes performed best according to the evaluation.
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET Journal
This document evaluates the performance of various classification algorithms (logistic regression, K-nearest neighbors, decision tree, random forest, support vector machine, naive Bayes) on a heart disease dataset. It provides details on each algorithm and evaluates their performance based on metrics like confusion matrix, precision, recall, F1-score and accuracy. The results show that naive Bayes had the best performance in correctly classifying samples with an accuracy of 80.21%, while SVM had the worst at 46.15%. In general, random forest and naive Bayes performed best according to the evaluation.
The presentation covers the application of a machine learning approach to classification and regression for modelling the expected loss in P&C insurance.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
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.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
Handling Imbalanced Data: SMOTE vs. Random UndersamplingIRJET Journal
This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.
This document discusses data collection and analysis for simulation modeling. It addresses questions like what types of data to gather, how to gather and analyze data, and how to represent data in a simulation. The key types of data are structural, operational, and numerical. Data should be gathered through methods like questionnaires, observation, and interviews. Basic statistical analysis techniques are described for understanding data characteristics like center, spread, and outliers. Common probability distributions are also introduced for representing random variables in a simulation model.
The document discusses various machine learning algorithms and libraries in Python. It provides descriptions of popular libraries like Pandas for data analysis and Seaborn for data visualization. It also summarizes commonly used algorithms for classification and regression like random forest, support vector machines, neural networks, linear regression, and logistic regression. Additionally, it covers model evaluation metrics, pre-processing techniques, and the process of model selection.
— The automation of fault detection in material
science is getting popular because of less cost and time. Steel
plates fault detection is an important material science problem.
Data mining techniques deal with data analysis of large data.
Decision trees are very popular classifiers because of their simple
structures and accuracy. A classifier ensemble is a set of
classifiers whose individual decisions are combined in to classify
new examples. Classifiers ensembles generally perform better
than single classifier. In this paper, we show the application of
decision tree ensembles for steel plates faults prediction. The
results suggest that Random Subspace and AdaBoost.M1 are the
best ensemble methods for steel plates faults prediction with
prediction accuracy more than 80%. We also demonstrate that if
insignificant features are removed from the datasets, the
performance of the decision tree ensembles improve for steel
plates faults prediction. The results suggest the future
development of steel plate faults analysis tools by using decision
tree ensembles.
This document provides an overview of machine learning concepts including feature selection, dimensionality reduction techniques like principal component analysis and singular value decomposition, feature encoding, normalization and scaling, dataset construction, feature engineering, data exploration, machine learning types and categories, model selection criteria, popular Python libraries, tuning techniques like cross-validation and hyperparameters, and performance analysis metrics like confusion matrix, accuracy, F1 score, ROC curve, and bias-variance tradeoff.
Comparative study of various supervisedclassification methodsforanalysing def...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET - Rainfall Forecasting using Weka Data Mining ToolIRJET Journal
1. The document discusses using data mining tools like Naive Bayes, Decision Trees, K-Nearest Neighbors and Support Vector Machines to forecast rainfall using a dataset containing weather variables.
2. The algorithms were tested on a dataset containing 679 instances of weather data from Jaipur, India from 2016-2018. Naive Bayes achieved an accuracy of 80.56%, Decision Trees achieved 94.10% accuracy, KNN achieved 93.96% accuracy, and SVM achieved 93.66% accuracy.
3. The most accurate models for rainfall prediction based on this dataset and analysis were Decision Trees and K-Nearest Neighbors, which both achieved over 93% accuracy in their forecasts.
Bank - Loan Purchase Modeling
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EDA of the data available. Showcase the results using appropriate graphs
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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.
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Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Accelerating the Random Forest algorithm for commodity parallel- Mark Seligman
1. Accelerating the Random Forest algorithm for commodity parallel
hardware
Mark Seligman
Suiji
August 5, 2015
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 1 / 44
2. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 2 / 44
4. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 4 / 44
5. Arborist project
Began as proprietary implementation of Random Forest (TM) algorithm.
Aim was enhanced performance across a wide variety of hardware, data and workflows.
GPU acceleration a key concern.
Open-sourced and rewritten following dissolution of venture.
Arborist is the project name.
Pyborist is the Python implementation, under development.
Rborist is the R package.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 5 / 44
6. Project design goals
Language-agnostic, compiled core.
Minimal reliance on call-backs and external libraries.
Minimize data movement.
Ready extensibility.
Common source base for all spins.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 6 / 44
7. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 7 / 44
8. Binary decision trees, briefly
Prediction method presenting a series of true/false questions about the data.
Answer to given question determines which (of two) questions to pose next.
Successive T/F branching relationship justifies “tree” nomenclature.
Different data take different paths through the tree.
Terminal (or “leaf”) node in path reports score for that path (and data).
Can build single tree and refine: “boosting”.
Can build “forest” of (typically) 100 − 1000 trees.
Overall average (regression) or plurality (classification) derived from each tree’s score.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 8 / 44
9. Random Forests
Random Forest is trademarked, registered to Leo Breiman (dec.) and Adele Cutler.
Predicts or validates vector of data (“response”)
Numerical: “regression”.
Categorical: “classification”.
Trains on design matrix of observations: “predictor” columns.
Columns individually either numerical or categorical (“factors”).
Trees trained on randomly-selected (“bagged”) set of matrix rows.
Predictors sampled randomly throughout training - separately chosen for each node.
Validation on held-out subset: different for each tree.
Independent prediction on separately-provided test sets.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 9 / 44
10. Training as tree building
Begins with a root node, together with the bagged set.
Bagging: can view as indicator set of row indices, with multiplicities.
Subnode construction (“splitting”) is driven by information content.
Nodes with sufficient information branch into two new subnodes.
Branch is annotated with splitting criterion, determining its sense.
If no splitting, the node is terminal: leaf.
Tree construction can proceed depth-first, breadth-first, ...
Construction terminates when frontier nodes exhaust information content.
User may also constrain termination: node count, tree depth, node width, ...
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 10 / 44
11. Building trees: splitting as conditioning
Splitting has the consequence of partitioning the training data into progressively smaller
subsets.
Operationally, the splitting criterion conditions the data into complementary subspaces.
The left successor inherits the subspace satisfying the criterion.
The right successor inherits its complement.
From this perspective, the root node trains on all bagged observations.
Successor nodes, similarly, train on data conditioned by the parent.
As we’ll see, the conditioned subspaces can be characterized as row sections of the design.
In other words, the splitting criteria define successive bipartitions on row indices.
From this perspective, then, the algorithm would seem to terminate naturally.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 11 / 44
12. Splitting: predictor perspective
Splitting criteria are formulated as order or subset relations with respect to some
predictor:
E.g., numerical predictor: p <= 3.2 ? branch left : branch right.
Factor predictor: q ∈ {3, 8, 17} ? branch left : branch right.
At a given node, candidate criteria obtained over randomly-sampled set of predictors.
Each predictor evaluates a series of (L/R) trial subsets:
For numerical predictors, trials are distinct cuts in the linear order.
For factors, trials are partitions over the runs of identical predictor values.
Criterion derived from trial maximizing “impurity” (separation) on response.
The predictor/criterion pair best “separating” the response is chosen for splitting.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 12 / 44
13. Predictor ordering ⇐⇒ row index permutation
Trial score is a function only of response - evaluated according to predictor order.
Irrespective of predictor, a given node is scored over a unique set of response indices.
The role of the predictor is to dictate the order to walk the indices.
That is, predictor values play no role in scoring the trials.
Hence only predictor ranks (and runs), affect scoring.
Each trial, in particular the “winner”, determines a bipartition of predictor ranks.
The predictor ranks, in turn, define a bipartition of row indices:
One set of indices characterizes the left branch.
Its complement characterizes the right branch.
Throughout training, then, the frontier nodes train over a (highly-disconnected) partition
of the original bagged data, as row sections.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 13 / 44
14. Trial generation: 4 cases, divergent work loads
Predictor
Response Numerical Factor
Regression Index walk Index walk → run sets
(weighted variance) Run set sort
Run set walk: O(# runs)
Classification Index walk Index walk → run sets
(Gini gain) Run set walk: O(2# runs)
Index walks are linear in node width, but differ in state maintained.
Power set walks resort to sampling above ∼ 10 runs.
Binary classification walks runs linearly.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 14 / 44
15. Aside: performance is data-dependent
As with linear algebra, the appropriate treatment depends on the contents of the (design)
matrix: SVD, for example.
E.g., regression has regular access patterns and tends to run very quickly.
Constraints in response or predictor values - may benefit from numerical simplification.
Will ties play a significant role? - sparse data can train very quickly.
Custom implementations rely heavily on the answer to such questions.
It therefore makes sense to strive for extensibility and ease of customization.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 15 / 44
16. Data locality
Computers store data in hierarchy:
Registers.
Caches (L1 - L3).
RAM.
Disk.
CPU operates on registers.
Loading registers consumes many clock cycles, depending upon position in hierarchy.
Performance therefore best when data is spatially (hence temporally) local.
Similarly, loops over vectors most efficient when data in consecutive iterations separated
by predictable and short(ish) strides.
“Regular” access patterns allow compiler, and hence hardware, to do a good job.
Regularity is crucial for GPUs, which excel at performing identical operations on
contiguous data.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 16 / 44
17. Algorithm: observations
Splitting is “embarrassingly parallel”: trials can be evaluated on all nodes in the frontier,
and all candidate predictors, at the same time.
However, ranks corresponding to a node’s row indices vary with predictor.
Naive solution is to sort observations at each splitting step.
Approach used by early implementations.
Does not scale well.
Predictor ordering used repeatedly: suggests pre-ordering.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 17 / 44
18. Algorithm: cont.
With pre-ordering, index walk accumulates per-node state by row index lookup.
Original Arborist approach.
No data locality, as index lookup is irregular.
Large state budget: must be swapped as indexed node changes.
“Restaging”: maintain separately-sorted state vectors for each predictor, by node.
Begin with pre-sorted list, update via (stable) bipartition at each node.
Current Arborist approach. Data locality improves with tree depth.
Only modest amount of state to move: 16 bytes to include doubles.
Splitting becomes quite regular: next datum prefetchable by hardware.
Each node/predictor pair restageable on SIMD hardware
Partition using parallel scan.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 18 / 44
19. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 19 / 44
20. Organization
Compiled code with various language front-ends.
R was the driving language, but Python now under active development.
Front-end “bridges” wherever possible: Rcpp, Cython.
Minimal use of front-end call-backs: PRNG, sampling, sorting.
Common code base also supports GPU version, largely as a subtyped extension.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 20 / 44
21. Look and feel
Guided by existing packages. Many options the same, or similar.
Supports only numeric and factor data: leaves type-wrangling to the user.
Predictor sampling: continuous predProb (Bernoulli) - vs. discrete max features (w/o
replacement).
Breadth-first implementation; introduces specification of terminating level.
Introduces information-based stopping criterion, minRatio.
Unlimited (essentially) factor cardinality: blessing as well as curse.
Many useful package options remain to be implemented.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 21 / 44
22. Distinguishing features
Decoupling of splitting from row-lookup: restaging + highly regular node walk.
Both stages benefit from resulting data locality and regularity.
Restaging maintained as stable partition (amenable to SIMD parallelization).
Training produces lightweight, serial “pre-tree”.
Rich intermediate state: e.g., frontier maps reveal quantiles.
Amenability to workflow internalization: “loopless” behavior.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 22 / 44
23. Training wheels, early experience
Began with R front end.
Compare performance with randomForest package.
“Medium” data: large, but in-memory.
Speedups typically observed as row counts approach 500 − 1000.
Linear scaling with # predictors, # trees, as expected.
Log-linear with # rows, also as expected.
Regression much easier to accelerate than classification.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 23 / 44
24. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 24 / 44
25. Feature trials: Bernoulli vs. w/o replacement
German credit-scoring data1: binary response, 1000 rows.
7 numerical predictors.
13 categorical predictors, cardinalities range from 2 to 10.
Graphs to follow compare misprediction, execution times at various predictor selections.
Accuracy, as function of trial metric:
Bernoulli (blue) and w/o replacement (green) appear to track well.
Performance of Rborist generally 2-3 ×, in this regime.
1 Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 25 / 44
27. q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q q
q
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5 10 15
2.02.53.03.54.04.5
Execution time ratios: randomForest / Rborist
(equivalent) mtry
Ratio
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 27 / 44
28. Instructive user account
Arborist performance problem noted in recent blog post.2
Airline flight-delay data tested on various RF packages.
8 predictors; various row counts: 104, 105, 106, 107.
Slowdown appears due to error in large-cardinality sampling.
In fact, Github version had already repiared salient problem.
Nonetheless, suggests improvements either implemented or to-be.
2 “Benchmarking Random Forest Implementations”, Szilard Pafka, DataScience.LA, May 19,
2015.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 28 / 44
29. Account, cont.
Splitting now parallelized across all pairs, rather than by-predictor.
Class weighting to treat unbalanced data.
Binary classification with high-cardinality factors:
Replaces sampling with n log n method.
Points to need for more, and broader, testing.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 29 / 44
30. GPU: pilot study with University of Washington team
GWAS data provided by Dept. Global Health 3.
100 samples, up to ∼ 106
predictors.
Binary response: HIV detected or not.
Purely categorical predictors with cardinality = 3: SNPs.
Bespoke CPU and GPU versions spun off for the data set.
Each tree trained (almost) entirely on GPU.
Results illustrate potential - for highly regular data sets.
Drop-off on right is an artefact from copying data.frame.
3 Courtesy of Lingappam lab.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 30 / 44
31. CPU vs. GPU: execution time ratios of bespoke versions
q
q
q
q
q
q
q
q
0 50000 100000 150000 200000 250000
1520253035404550
CPU vs GPU: timing ratios (1000 trees)
Predictor count
Ratio
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 31 / 44
32. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 32 / 44
33. GPU-centric packages
ad hoc version not scalable as implemented: rewritten.
Restaging now implemented as stable partition via parallel scan.
Nvidia engineers concur with this solution, anticipate good scaling.
In general, though, data need not be so regular as this.
Mixed predictor types and multiple cardinalities present load-balancing challenges.
Dynamic parallelism option available for irregular workloads.
Predictor selection thwarts data locality: adjacent columns not necessarily used.
Lowest-hanging fruit may be isolated special cases such as SNP data.
Mark Seligman (Suiji) Accelerating the Random Forest algorithm for commodity parallel hardware August 5, 2015 33 / 44
34. GPU vs. CPU
Highly regular regression/numeric case may perform well on GPU.
On-GPU transpose: restaging, splitting employ different major orderings.
For now: split on CPU and restage (highly regular) on GPU.
Multiple trees can be restaged at once via software pipeline:
Masks transfer latency by overlapping training of multiple trees.
Keeps CPU busy by dispatching less-regular tasks to multiple cores.
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35. CPU-level parallelism
Original work focused on predictor-level parallelism, emphasizing wider data sets.
Node-level parallelism has emerged as equal player (e.g., flight-delay data).
But with high core count and closely-spaced data, false sharing looms as potential threat.
Infrastructure now in place to support hierarchical parellization:
Head node orders predictors and scatters copies.
Multiple nodes each train blocks of trees on multicore hardware.
GPU participation also possible.
Head node gathers pretrees, builds forest, validates.
Remaining implementation chiefly a matter of scheduling and tuning.
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36. CPU: load balancing
Mixed factor, numerical predictors offer greatest challenge, especially for classification.
In some cases, may benefit from parallelizing trial generations themselves.
Irrespective of data types, inter-level pass following splitting is inherently sequential.
May make sense to pipeline: overlap splitting of one tree with interlevel of another.
N.B.: Much more performance-testing is needed to investigate these scenarios.
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37. Additional projects
Sparse internal representation.
Inchoate; main challenge is defining interface.
NA handling.
Some variants easier to implement than others.
Post-processing: facilitate use by other utilities.
Feature contributions.
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38. Pyborist: goals
Encourage flexing, testing by broader ML community.
Honor precedent of scikit-learn: features, style.
Provide R-like abstractions: data frames and factors.
Attempt to minimize impact of host language on user data.
Stress software organization and design.
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39. Pyborist: key ingredients
Cython bridge: emphasis on compilation.
Pandas: “dataframe” and “category” essential.
NumPy: PRNG, sort and sampling call-backs.
Considered other options: SWIG, CFFI, ctypes ...
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40. 1 Outline
2 Introduction
3 Random Forests
4 Implementation
5 Examples and anecdotes: R
6 Ongoing work
7 Summary and future work
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41. Summary
Only a few rigid design principles:
Constrain data movement.
Language-agnostic, compiled core implementation.
Common source base.
Plenty of opportunities for improvement.
Load balancing appears to be lowest-hanging fruit: both CPU and GPU.
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42. Longer term
Solicit help, comments from the community.
Expanded use of templating: large, small types.
Out-of-memory support.
Generalized dispatch, fat binaries.
Plugins for other splitting methods: non-Gini.
Internalize additional workflows.
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43. Acknowledgments
Stephen Elston, Quanta Analytics.
Abraham Flaxman, Dept. Global Health, U.W.
Seattle PuPPy.
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