iGraph analysis April 10, 2015 Numerics iGraph; a graph layout and analysis package for R The iGraph package created by Gábor Csárdi is a huge library for everything graphs, graph layout and graph analysis in R. Much like any other graph library, it implies a learning curve and some math insights but it’s also refreshingly […]
Introduction to information system Forests and other Ensemble Methods… Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Recap: Regression and Classification Trees Grow a binary tree • At each node, “split” the data into two “daughter” nodes. • Splits are chosen using a splitting criterion. • Bottom nodes are “terminal” nodes.
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Introduction to information system Linear Regression Deema Abdal Hafeth Bowei Chen CMP3036M/CMP9063M Data Science 2016 – 2017 Today’s Objectives • Simple Linear Regression – Formulation – Parameters Estimation: Least Square Estimation (LSE) • Multiple linear Regression • Appendix: Derivation of LSE References • James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction
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ANLY 502 Assignment 3 Version 1.0 Note: Read this assignment online at https://bitbucket.org/ANLY502/anly502_2017_spring/src/HEAD/A3/?at=master In this problem set, you will analyze a network flow dataset that was created for this course. This dataset is based on the mtr command, a popular open source traceroute command that has an interactive, character-based display. The program performs multiple traceroute operations over time and displays
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Introduction to information system Questions & Answers Tips for Assessment Item 1 Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Don’t know how to start your assignment? Mining the data first! Candidate models Key Stages of This Data Mining Task Data Preparation Model Training Model Evaluation In this stage, two steps
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Introduction to information system Popular Distributions (2/2) Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science • Univariate Distributions – Discrete Distributions • Uniform • Bernoulli • Binomial • Poisson – Continuous Distributions • Uniform • Exponential • Normal/Gaussian • Multivariate Distributions – Multivariate Normal Distribution Objectives Today’s Objectives Popular Distributions (2/2)
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Introduction to information system Introduction to Data Science Semester B Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Module Information Title Data Science Code CMP3036M/CMP9063M Semester 2016-2017 Semesters A & B Assessment CMP3036M: Assignment (50%) + Assignment (50%) CMP9063M: Assignment (40%) + Assignment (40%) + Report (20%) Delivery Team in Semester
Introduction to information system Model Selection Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science • Basic Setup of the Learning from Data • Cross-Validation Methods • Appendix A: Testing-Based/Stepwise Procedures • Appendix B: Criterion-Based Procedures Today’s Objectives Limitation of Linear Regression Price Fullbase 1 420 1 2 385 0 3 495
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LinearRegression-Workshop2 Data Science Workshop: Week 4¶ This week we will use the theory about linear regression that we have learned last and this week. We will recap derivatives, introduce vectors and matrices in python and derive and implement the least squares solution. Subsequently, we will look at polynomial regression using the least squares solution derived
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Introduction to information system Popular Distributions (1/2) Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science • Univariate Distributions – Discrete Distributions • Uniform • Bernoulli • Binomial • Poisson – Continuous Distributions • Uniform • Normal/Gaussian • Exponential • Multivariate Distributions – Multivariate Normal Distribution Objectives Today’s Objectives Popular Distributions (1/2)
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