Toward a Unified Approach to Fitting Loss Models
•Download as PPT, PDF•
0 likes•334 views
The document discusses developing a unified approach for fitting loss models to insurance data. It proposes using a limited set of standard distributions, including the exponential, gamma, lognormal and Pareto distributions. These distributions could be combined in mixtures to better fit different parts of the data. The document presents an example of fitting a mixture of a lognormal and exponential distribution to insurance payment data. Graphs and statistical tests show the model provides an acceptable fit to the data. Automating the process of fitting mixtures and comparing models is an area for further development.
Report
Share
Report
Share
![Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Regression Analysis and model comparison on the Boston Housing Data
Creation of regression models to predict the median housing price using the Boston Housing dataset. Models used: Generalized linear model, generalized additive model, artificial neural networks, regression tree
3.3 hierarchical methods
Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.
3.2 partitioning methods
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
Data Compression in Data mining and Business Intelligencs
This document discusses various techniques for numerosity reduction in data mining. Numerosity reduction aims to reduce the volume of data while maintaining integrity. Methods include parametric approaches like regression and log-linear models, and non-parametric methods such as histograms, clustering, sampling, and data cube aggregation. Histograms bin data into buckets to store average values, while clustering partitions data into groups. Sampling obtains a representative subset of data. Data cube aggregation precomputes and stores multidimensional summarized data. Together, these techniques provide more efficient analysis of large datasets.
Grid based method & model based clustering method
The document discusses several grid-based, density-based, and conceptual clustering algorithms. Grid-based approaches like STING and WAVECLUSTER cluster data by quantizing space into grids or cells. CLIQUE uses a grid-based approach to identify dense units of data. Conceptual clustering algorithms like COBWEB create hierarchical cluster trees to classify objects based on attribute probabilities.
Machine Learning Project
This document discusses the implementation of the CLARANS K-medoid clustering algorithm in Java. It also explores using the Torch machine learning library and Torch3Vision for image processing tasks like face detection. Different distance functions for clustering algorithms are analyzed and how they relate to spatial versus spectral patterns in images. The document presents results of clustering a sample image using K-medoid while varying the preference for spatial or spectral patterns.
3.6 constraint based cluster analysis
Constraint-based clustering finds clusters that satisfy user-specified constraints, such as the expected number of clusters or minimum/maximum cluster size. It considers obstacles like rivers or roads that require redefining distance functions. Clustering algorithms are adapted to handle obstacles by using visibility graphs and triangulating regions to reduce distance computation costs. Semi-supervised clustering uses some labeled data to initialize and modify algorithms like k-means to satisfy pairwise constraints.
Hierarchical clustering
Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. There are two types of hierarchical clustering methods: divisive, which starts with all observations in one cluster and splits recursively; and agglomerative, which starts with each observation in its own cluster and merges pairs of clusters as it moves up the hierarchy. Popular algorithms like BIRCH and CURE were introduced to improve upon hierarchical clustering methods by handling large datasets more efficiently and accounting for clusters with irregular shapes. However, hierarchical clustering still has weaknesses like inability to undo merges and poor scaling to large datasets.
Recommended
Regression Analysis and model comparison on the Boston Housing Data
Creation of regression models to predict the median housing price using the Boston Housing dataset. Models used: Generalized linear model, generalized additive model, artificial neural networks, regression tree
3.3 hierarchical methods
Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.
3.2 partitioning methods
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
Data Compression in Data mining and Business Intelligencs
This document discusses various techniques for numerosity reduction in data mining. Numerosity reduction aims to reduce the volume of data while maintaining integrity. Methods include parametric approaches like regression and log-linear models, and non-parametric methods such as histograms, clustering, sampling, and data cube aggregation. Histograms bin data into buckets to store average values, while clustering partitions data into groups. Sampling obtains a representative subset of data. Data cube aggregation precomputes and stores multidimensional summarized data. Together, these techniques provide more efficient analysis of large datasets.
Grid based method & model based clustering method
The document discusses several grid-based, density-based, and conceptual clustering algorithms. Grid-based approaches like STING and WAVECLUSTER cluster data by quantizing space into grids or cells. CLIQUE uses a grid-based approach to identify dense units of data. Conceptual clustering algorithms like COBWEB create hierarchical cluster trees to classify objects based on attribute probabilities.
Machine Learning Project
This document discusses the implementation of the CLARANS K-medoid clustering algorithm in Java. It also explores using the Torch machine learning library and Torch3Vision for image processing tasks like face detection. Different distance functions for clustering algorithms are analyzed and how they relate to spatial versus spectral patterns in images. The document presents results of clustering a sample image using K-medoid while varying the preference for spatial or spectral patterns.
3.6 constraint based cluster analysis
Constraint-based clustering finds clusters that satisfy user-specified constraints, such as the expected number of clusters or minimum/maximum cluster size. It considers obstacles like rivers or roads that require redefining distance functions. Clustering algorithms are adapted to handle obstacles by using visibility graphs and triangulating regions to reduce distance computation costs. Semi-supervised clustering uses some labeled data to initialize and modify algorithms like k-means to satisfy pairwise constraints.
Hierarchical clustering
Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. There are two types of hierarchical clustering methods: divisive, which starts with all observations in one cluster and splits recursively; and agglomerative, which starts with each observation in its own cluster and merges pairs of clusters as it moves up the hierarchy. Popular algorithms like BIRCH and CURE were introduced to improve upon hierarchical clustering methods by handling large datasets more efficiently and accounting for clusters with irregular shapes. However, hierarchical clustering still has weaknesses like inability to undo merges and poor scaling to large datasets.
slides
This document provides an overview of machine learning algorithms for classification and clustering, addressing privacy issues in applying these algorithms to sensitive data. It discusses techniques for single dataset publishing and distributed analysis across multiple private datasets using cryptographic protocols or data perturbation to preserve privacy. Key algorithms covered include decision trees, naive Bayes, k-nearest neighbors, support vector machines, boosting, and clustering methods like k-means.
Chapter8
This document discusses different clustering methods in data mining. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based clustering methods. Finally, it provides details on partitioning methods like k-means and k-medoids clustering algorithms.
Dataa miining
The document discusses various clustering methods used in data mining. It describes partitioning methods like k-means and k-medoids which group data into a set number of clusters based on distance between data points. Hierarchical clustering creates nested clusters based on distance metrics. Density-based methods find clusters based on connectivity and density. Model-based clustering fits a model to each cluster.
Instance based learning
Instance-based learning algorithms like k-nearest neighbors (KNN) and locally weighted regression are conceptually straightforward approaches to function approximation problems. These algorithms store all training data and classify new query instances based on similarity to near neighbors in the training set. There are three main approaches: lazy learning with KNN, radial basis functions using weighted methods, and case-based reasoning. Locally weighted regression generalizes KNN by constructing an explicit local approximation to the target function for each query. Radial basis functions are another related approach using Gaussian kernel functions centered on training points.
lecture_mooney.ppt
This document provides an overview of machine learning and various machine learning techniques. It discusses what machine learning is, different types of learning tasks like classification and regression, how performance is measured, and different types of training experiences like direct supervision and reinforcement learning. It then covers specific machine learning algorithms like classification using Rocchio's algorithm, nearest neighbor learning, Bayesian learning approaches, and text categorization using naive Bayes.
Types of clustering and different types of clustering algorithms
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
Clustering
This document describes a new clustering tool for data mining called RAPID MINER. It discusses the need for clustering in applications like customer segmentation. The project aims to develop a new clustering algorithm using preprocessing techniques like removing null values and redundant data. It will implement clustering to distribute data into groups so that association is strong within clusters and weak between clusters. The document compares the new tool to Weka, discusses how it uses KD trees to improve efficiency over K-means clustering, and concludes that the new algorithm chooses better starting clusters and filters data faster using KD trees.
lab report 4
1. This document contains a lab report submitted by Selase Kwami for a Geography course. It includes questions about performing GIS functions like clip, spatial join, buffer analysis, and summary statistics in ArcMap using shapefiles of schools and Accra metro data.
2. The answers explain how to perform each function in ArcMap and the meaning and purpose of each function. Clip extracts part of a dataset shape or raster based on a polygon. Spatial join links attribute data from one layer to another based on their spatial relationship. Buffer creates zones around points, lines or polygons. Summary statistics calculates metrics like mean, sum or count for numeric fields.
3. Images are included showing examples of output for each
Lec4 Clustering
This document provides an overview of clustering techniques and presents a sample MapReduce implementation of K-Means clustering and Canopy clustering on a large dataset. It discusses how clustering can be used to group large, high-dimensional datasets and describes hierarchical and partitional clustering algorithms. It also outlines the steps taken in the MapReduce implementation to distribute the clustering computation across multiple nodes.
Unsupervised learning clustering
Unsupervised Learning_Cluster Techniques
Concepts & Comparision of hierarchical & k-means clustering
Clustering (from Google)
Distributed Computing Seminar Lecture 4 provided an overview of clustering algorithms and techniques. It discussed how clustering is used to group related data in applications like Google News and Amazon. It described hierarchical and partitional clustering algorithms and the k-means clustering algorithm. The lecture also introduced canopy clustering as a preliminary step that can help parallelize computation for clustering large datasets using MapReduce. It provided an example of how to efficiently partition a large movie ratings dataset into clusters using canopy clustering, k-means clustering, and MapReduce.
Selection K in K-means Clustering
This document summarizes a paper presentation on selecting the optimal number of clusters (K) for k-means clustering. The paper proposes a new evaluation measure to automatically select K without human intuition. It reviews existing methods, analyzes factors influencing K selection, describes the proposed measure, and applies it to real datasets. The method was validated on artificial and benchmark datasets. It aims to suggest multiple K values depending on the required detail level for clustering. However, it is computationally expensive for large datasets and the data used may not reflect real complexity.
Af4201214217
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Clustering
Clustering algorithms are a type of unsupervised learning that groups unlabeled data points together based on similarities. There are many different clustering methods that can handle numeric and/or symbolic data in either hierarchical or flat clustering structures. One simple and commonly used algorithm is k-means clustering, which assigns data points to k clusters based on minimizing distances between points and assigned cluster centers. K-means clustering has advantages of being simple and automatically assigning data points but has disadvantages such as needing to pre-specify the number of clusters and being sensitive to outliers.
Parameter Optimisation for Automated Feature Point Detection
Parameter optimization for an automated feature point detection model was explored. Increasing the number of random displacements up to 20 improved performance but additional increases did not. Larger patch sizes consistently improved performance. Increasing the number of decision trees did not affect performance for this single-stage model, unlike previous findings for a two-stage model. Overall, some parameter tuning was found to enhance the model's accuracy but not all parameters significantly impacted results.
Clustering
This document discusses different types of clustering techniques used in machine learning. It describes clustering as an unsupervised learning method that groups similar data points together. The main types covered are hard clustering, soft clustering, density-based clustering, hierarchical clustering, and partitioning-based clustering like k-means. K-means and hierarchical clustering are explained in more detail, including examples of how they work. Agglomerative and divisive hierarchical clustering are two approaches defined.
Pillar k means
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
Nearest neighbors
This document provides an overview and agenda for a presentation on nearest neighbors algorithms. It will cover fundamentals of nearest neighbors, using nearest neighbors for unsupervised learning, classification, and regression. Specific topics that will be discussed include k-nearest neighbors algorithms, algorithms to store training data like brute force and k-d trees, nearest neighbors classification using k-nearest neighbors and radius-based classifiers, nearest neighbors regression, and the nearest centroid classifier.
Big data Clustering Algorithms And Strategies
The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
Cure, Clustering Algorithm
The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.
Моніторинг ЦНАПів у Львові: результати та зміни. Наталя Міхнова
Презентація Наталі Міхнової для вебінару 3 червня.
More Related Content
What's hot
slides
This document provides an overview of machine learning algorithms for classification and clustering, addressing privacy issues in applying these algorithms to sensitive data. It discusses techniques for single dataset publishing and distributed analysis across multiple private datasets using cryptographic protocols or data perturbation to preserve privacy. Key algorithms covered include decision trees, naive Bayes, k-nearest neighbors, support vector machines, boosting, and clustering methods like k-means.
Chapter8
This document discusses different clustering methods in data mining. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based clustering methods. Finally, it provides details on partitioning methods like k-means and k-medoids clustering algorithms.
Dataa miining
The document discusses various clustering methods used in data mining. It describes partitioning methods like k-means and k-medoids which group data into a set number of clusters based on distance between data points. Hierarchical clustering creates nested clusters based on distance metrics. Density-based methods find clusters based on connectivity and density. Model-based clustering fits a model to each cluster.
Instance based learning
Instance-based learning algorithms like k-nearest neighbors (KNN) and locally weighted regression are conceptually straightforward approaches to function approximation problems. These algorithms store all training data and classify new query instances based on similarity to near neighbors in the training set. There are three main approaches: lazy learning with KNN, radial basis functions using weighted methods, and case-based reasoning. Locally weighted regression generalizes KNN by constructing an explicit local approximation to the target function for each query. Radial basis functions are another related approach using Gaussian kernel functions centered on training points.
lecture_mooney.ppt
This document provides an overview of machine learning and various machine learning techniques. It discusses what machine learning is, different types of learning tasks like classification and regression, how performance is measured, and different types of training experiences like direct supervision and reinforcement learning. It then covers specific machine learning algorithms like classification using Rocchio's algorithm, nearest neighbor learning, Bayesian learning approaches, and text categorization using naive Bayes.
Types of clustering and different types of clustering algorithms
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
Clustering
This document describes a new clustering tool for data mining called RAPID MINER. It discusses the need for clustering in applications like customer segmentation. The project aims to develop a new clustering algorithm using preprocessing techniques like removing null values and redundant data. It will implement clustering to distribute data into groups so that association is strong within clusters and weak between clusters. The document compares the new tool to Weka, discusses how it uses KD trees to improve efficiency over K-means clustering, and concludes that the new algorithm chooses better starting clusters and filters data faster using KD trees.
lab report 4
1. This document contains a lab report submitted by Selase Kwami for a Geography course. It includes questions about performing GIS functions like clip, spatial join, buffer analysis, and summary statistics in ArcMap using shapefiles of schools and Accra metro data.
2. The answers explain how to perform each function in ArcMap and the meaning and purpose of each function. Clip extracts part of a dataset shape or raster based on a polygon. Spatial join links attribute data from one layer to another based on their spatial relationship. Buffer creates zones around points, lines or polygons. Summary statistics calculates metrics like mean, sum or count for numeric fields.
3. Images are included showing examples of output for each
Lec4 Clustering
This document provides an overview of clustering techniques and presents a sample MapReduce implementation of K-Means clustering and Canopy clustering on a large dataset. It discusses how clustering can be used to group large, high-dimensional datasets and describes hierarchical and partitional clustering algorithms. It also outlines the steps taken in the MapReduce implementation to distribute the clustering computation across multiple nodes.
Unsupervised learning clustering
Unsupervised Learning_Cluster Techniques
Concepts & Comparision of hierarchical & k-means clustering
Clustering (from Google)
Distributed Computing Seminar Lecture 4 provided an overview of clustering algorithms and techniques. It discussed how clustering is used to group related data in applications like Google News and Amazon. It described hierarchical and partitional clustering algorithms and the k-means clustering algorithm. The lecture also introduced canopy clustering as a preliminary step that can help parallelize computation for clustering large datasets using MapReduce. It provided an example of how to efficiently partition a large movie ratings dataset into clusters using canopy clustering, k-means clustering, and MapReduce.
Selection K in K-means Clustering
This document summarizes a paper presentation on selecting the optimal number of clusters (K) for k-means clustering. The paper proposes a new evaluation measure to automatically select K without human intuition. It reviews existing methods, analyzes factors influencing K selection, describes the proposed measure, and applies it to real datasets. The method was validated on artificial and benchmark datasets. It aims to suggest multiple K values depending on the required detail level for clustering. However, it is computationally expensive for large datasets and the data used may not reflect real complexity.
Af4201214217
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Clustering
Clustering algorithms are a type of unsupervised learning that groups unlabeled data points together based on similarities. There are many different clustering methods that can handle numeric and/or symbolic data in either hierarchical or flat clustering structures. One simple and commonly used algorithm is k-means clustering, which assigns data points to k clusters based on minimizing distances between points and assigned cluster centers. K-means clustering has advantages of being simple and automatically assigning data points but has disadvantages such as needing to pre-specify the number of clusters and being sensitive to outliers.
Parameter Optimisation for Automated Feature Point Detection
Parameter optimization for an automated feature point detection model was explored. Increasing the number of random displacements up to 20 improved performance but additional increases did not. Larger patch sizes consistently improved performance. Increasing the number of decision trees did not affect performance for this single-stage model, unlike previous findings for a two-stage model. Overall, some parameter tuning was found to enhance the model's accuracy but not all parameters significantly impacted results.
Clustering
This document discusses different types of clustering techniques used in machine learning. It describes clustering as an unsupervised learning method that groups similar data points together. The main types covered are hard clustering, soft clustering, density-based clustering, hierarchical clustering, and partitioning-based clustering like k-means. K-means and hierarchical clustering are explained in more detail, including examples of how they work. Agglomerative and divisive hierarchical clustering are two approaches defined.
Pillar k means
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
Nearest neighbors
This document provides an overview and agenda for a presentation on nearest neighbors algorithms. It will cover fundamentals of nearest neighbors, using nearest neighbors for unsupervised learning, classification, and regression. Specific topics that will be discussed include k-nearest neighbors algorithms, algorithms to store training data like brute force and k-d trees, nearest neighbors classification using k-nearest neighbors and radius-based classifiers, nearest neighbors regression, and the nearest centroid classifier.
Big data Clustering Algorithms And Strategies
The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
Cure, Clustering Algorithm
The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.
What's hot (20)
Types of clustering and different types of clustering algorithms
Types of clustering and different types of clustering algorithms
Parameter Optimisation for Automated Feature Point Detection
Parameter Optimisation for Automated Feature Point Detection
Viewers also liked
Моніторинг ЦНАПів у Львові: результати та зміни. Наталя Міхнова
Презентація Наталі Міхнової для вебінару 3 червня.
HCR Alert jun_2012_guidance_fsa_limit
The IRS has issued guidance on implementing the $2,500 limit on employees' pre-tax contributions to health flexible spending accounts (FSAs) as required by the 2010 health care reform law. Key points include: the limit applies on a plan year basis for cafeteria plans beginning after 2012; employers have until 2014 to amend plans to reflect the limit but must administer plans in accordance with it starting in 2013; and relief is provided for excess contributions due to reasonable mistakes.
E107 Open Education Practice and Potential: Session 1
Session 1 for Education E-107, Open Education Practice and Potential, Spring 2011 (Harvard University Extension) taught by M.S. Vijay Kumar and Brandon Muramatsu.
Beba
El método Simplex es un procedimiento iterativo para resolver problemas de programación lineal maximizando o minimizando una función objetivo sujeta a restricciones. Comienza con una solución factible inicial y mejora sucesivamente la solución encontrando un vértice adyacente que mejore el valor de la función hasta alcanzar la solución óptima.
Intro to Using Commons Groups for Internal Communications
This brief PowerPoint presentation was created to provide MCAS employees with a basic understanding of the capabilities of Commons Groups.
Viewers also liked (6)
Моніторинг ЦНАПів у Львові: результати та зміни. Наталя Міхнова
Моніторинг ЦНАПів у Львові: результати та зміни. Наталя Міхнова
E107 Open Education Practice and Potential: Session 1
E107 Open Education Practice and Potential: Session 1
Intro to Using Commons Groups for Internal Communications
Intro to Using Commons Groups for Internal Communications
Similar to Toward a Unified Approach to Fitting Loss Models
Intro to Model Selection
This document provides an introduction to statistical model selection. It discusses various approaches to model selection including predictive risk, Bayesian methods, information theoretic measures like AIC and MDL, and adaptive methods. The key goals of model selection are to understand the bias-variance tradeoff and select models that offer the best guaranteed predictive performance on new data. Model selection aims to find the right level of complexity to explain patterns in available data while avoiding overfitting.
German credit score shivaram prakash
The document analyzes models for predicting loan default using a German credit dataset. It fits generalized linear models, generalized additive models, linear discriminant analysis, and classification trees to the data. Based on out-of-sample testing, the linear discriminant analysis model provided the best results with a minimum misclassification rate of 0.40 and maximum area under the ROC curve of 0.867. However, the performance of all the models was quite similar.
Model selection
This document discusses methods for selecting variables and regularizing models in linear regression. It begins by introducing linear regression models and discussing their advantages and limitations. It then covers several approaches to improve upon ordinary least squares, including subset selection methods like best subset selection and stepwise selection, and shrinkage methods like ridge regression. Ridge regression fits a model with all variables but shrinks their coefficients towards zero to improve predictive performance. The document provides examples applying these methods to a credit scoring data set.
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
This document analyzes a dataset of diabetes records from 130 US hospitals from 1999-2008 using various statistical data analysis and machine learning techniques. It first performs dimensionality reduction using principal component analysis (PCA) and multidimensional scaling (MDS). It then clusters the data using hierarchical clustering and k-means clustering. Cluster validity is assessed using precision. Spectral clustering is also applied and validated using Dunn and Davies-Bouldin indexes, with complete linkage diameter performing best.
Max Kuhn's talk on R machine learning
Twitter: @NycDataSci
Learn with our NYC Data Science Program (weekend courses for working professionals and 12 week full time for whom are advancing their career into Data Science)
Our next 12-Week Data Science Bootcamp starts in Jun. (Deadline to apply is May 1st, all decisions will be made by May 15th.)
====================================
Max Kuhn, Director is Nonclinical Statistics of Pfizer and also the author of Applied Predictive Modeling.
He will join us and share his experience with Data Mining with R.
Max is a nonclinical statistician who has been applying predictive models in the diagnostic and pharmaceutical industries for over 15 years. He is the author and maintainer for a number of predictive modeling packages, including: caret, C50, Cubist and AppliedPredictiveModeling. He blogs about the practice of modeling on his website at ttp://appliedpredictivemodeling.com/blog
---------------------------------------------------------
His Feb 18th course can be RSVP at NYC Data Science Academy.
Syllabus
Predictive Modeling using R
Description
This class will get attendees up to speed in predictive modeling using the R programming language. The goal of the course is to understand the general predictive modeling process and how it can be implemented in R. A selection of important models (e.g. tree-based models, support vector machines) will be described in an intuitive manner to illustrate the process of training and evaluating models.
Prerequisites:
Attendees should have a working knowledge of basic R data structures (e.g. data frames, factors etc) and language fundamentals such as functions and subsetting data. Understanding of the content contained in Appendix B sections B1 though B8 of Applied Predictive Modeling (free PDF from publisher [1]) should suffice.
Outline:
- An introduction to predictive modeling
- R and predictive modeling: the good and bad
- Illustrative example
- Measuring performance
- Data splitting and resampling
- Data pre-processing
- Classification trees
- Boosted trees
- Support vector machines
If time allows, the following topics will also be covered
- Parallel processing
- Comparing models
- Feature selection
- Common pitfalls
Materials:
Attendees will be provided with a copy of Applied Predictive Modeling[2] as well as course notes, code and raw data. Participants will be able to reproduce the examples described in the workshop.
Attendees should have a computer with a relatively recent version of R installed.
About the Instructor:
More about Max's work:
[1] http://rd.springer.com/content/pdf/bbm%3A978-1-4614-6849-3%2F1.pdf
[2] http://appliedpredictivemodeling.com
Guide for building GLMS
This document provides a guide for building generalized linear models (GLMs) in R to accurately model insurance claim frequency and severity. It outlines steps for data preparation, including handling missing data, transforming variables, and removing outliers. It then discusses modeling count/frequency data with Poisson or negative binomial models including an offset for exposure. Severity is typically modeled with a gamma or normal distribution. The document provides examples of investigating interactions and comparing models using AIC, BIC, and residual analysis.
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.
November, 2006 CCKM'06 1
- Kernel methods allow linear learning algorithms to be applied to non-linear problems by mapping data into high-dimensional feature spaces. This overcomes limitations of insufficient capacity in linear models.
- Both frequentist and Bayesian approaches to kernel methods have been developed. Frequentist approaches analyze generalization error, while Bayesian approaches use probabilistic modeling and inference.
- Support vector machines optimize the margin between classes in feature space, improving generalization. The dual formulation allows computation using kernel functions without explicitly working in feature space.
Lecture 17: Supervised Learning Recap
This document provides a summary of supervised learning techniques and an introduction to unsupervised learning methods. It recaps kernel methods and reviews concepts in supervised learning like linear regression, logistic regression, graphical models, hidden Markov models, neural networks, and support vector machines. It then introduces clustering algorithms like k-means clustering, soft k-means, Gaussian mixture models, and expectation maximization. It also discusses using graphical models and hidden Markov models with latent variables for unsupervised learning tasks.
Probability density estimation using Product of Conditional Experts
This document discusses probability density estimation using a product of conditional experts model. It summarizes that density estimation constructs a probability distribution function from observed data to understand the underlying pattern. A product of conditional experts model is proposed, where simple classification models like logistic regression are used as experts to estimate the conditional probability. The experts are combined by multiplying their probabilities. The model is trained using gradient ascent to maximize the log probability. When evaluated on artificial and real datasets, the product of conditional experts model is shown to learn distributions close to the true distributions and generalize better than linear and non-linear baseline models. The document also explores applying the model to outlier detection.
Multiple Regression
Designed to construct a statistical model describing the impact of a two or more quantitative factors on a dependent variable. The fitted model may be used to make predictions, including confidence limits and/or prediction limits. Residuals may also be plotted and influential observations identified.
Diabetes data - model assessment using R
This document analyzes different regression models to predict disease progression in diabetes patients using baseline measurements. It finds that a lasso regression model with predictors for sex, BMI, blood pressure, HDL, triglycerides and glucose performs best, with the lowest test error. This model is recommended for further consideration due to its predictive accuracy and interpretable sparse coefficients.
P & C Reserving Using GAMLSS
- The document proposes using Generalized Additive Models for Location, Scale and Shape (GAMLSS) as an alternative methodology for claims reserving to provide both a point estimate and a measure of uncertainty.
- It describes how GAMLSS allows modeling of multiple distribution parameters, such as modeling the variance of incremental payments as a function of development year, to better fit the data.
- Numerical results on an example claims triangle show that GAMLSS provides a better fit than classical generalized linear models as measured by a lower GAIC, and results in a lower estimated variability for claims reserves.
report
This document summarizes the analysis of data from a pharmaceutical company to model and predict the output variable (titer) from input variables in a biochemical drug production process. Several statistical models were evaluated including linear regression, random forest, and MARS. The analysis involved developing blackbox models using only controlled input variables, snapshot models using all input variables at each time point, and history models incorporating changes in input variables over time to predict titer values. Model performance was compared using cross-validation.
Model Selection Techniques
This document discusses different techniques for selecting machine learning models, including random train/test splitting, resampling methods like k-fold cross-validation and bootstrap, and probabilistic measures. Resampling techniques like k-fold cross-validation estimate error by evaluating models on out-of-sample data. Probabilistic measures consider both a model's performance and complexity, seeking to balance fit and simplicity. Common probabilistic measures mentioned are the Akaike Information Criterion, Bayesian Information Criterion, Minimum Description Length, and Structural Risk Minimization.
SVM - Functional Verification
This document discusses using unsupervised support vector analysis to increase the efficiency of simulation-based functional verification. It describes applying an unsupervised machine learning technique called support vector analysis to filter redundant tests from a set of verification tests. By clustering similar tests into regions of a similarity metric space, it aims to select the most important tests to verify a design while removing redundant tests, improving verification efficiency. The approach trains an unsupervised support vector model on an initial set of simulated tests and uses it to filter future tests by comparing them to support vectors that define regions in the similarity space.
Auto MPG Regression Analysis
Econometrics Project to Study the relationship between Horsepower,Displacement,Acceleration,Cylinders on Miles Per Gallon(MPG)
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
AUTO MPG Regression Analysis
Conducted Regression Analysis to study the relationship between Horsepower, Displacement, Cylinders, Acceleration on Miles Per Gallon (Mpg).
Performed Multiple Transformations (Log Transformations, Dummy Variables) and found out that the Adjusted R-Squared improved with each model.
Conducted heteroskedasticity checks and corrected the heteroskedasticity problem using robust standard errors.
07 dimensionality reduction
07 April Stefano Cavuoti: The curse of dimensionality
On the challenges of very large descriptor spaces
Similar to Toward a Unified Approach to Fitting Loss Models (20)
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Data Science - Part V - Decision Trees & Random Forests
Data Science - Part V - Decision Trees & Random Forests
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
Toward a Unified Approach to Fitting Loss Models
- 1. Toward a unified approach to fitting loss models Jacques Rioux and Stuart Klugman, for presentation at the IAC, Feb. 9, 2004
- 15. Empirical cdf
- 20. CDF plot with bounds
- 24. Histogram plot
- 31. Several models Model Loglike A-D K-S Chi-sq SBC Exp -628.23 1.2245 0.9739 0.1054 -630.53 Ln -626.26 0.6682 0.9375 0.2126 -630.87 Gam -627.35 0.8369 1.0355 0.2319 -631.96 L/E -623.77 0.2579 0.5829 0.5608 -632.98 G/E -623.64 0.2804 0.5773 0.5260 -632.85 L/E/E -623.39 0.1484 0.4494 0.3472 -637.21 G/E/E -623.26 0.1353 0.4652 0.3348 -637.08