Linear Regression Linear Regression Mariia Okuneva The main difference to Econometrics course: when using linear models in Econometrics course, we often emphasize distributional results useful for hypothesis testing. Our main goal is to explain a relationship. Now, we use linear regression as a tool to predict. We seek a model which minimizes errors! Remember that […]
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(Linear) Classification Data mining Prof. Dr. Matei Demetrescu Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 40 Predicting qualitative responses Recall, qualitative variables take values in an unordered set C, such as: eye color∈{brown, blue, green} email∈{spam, ham}. The classification task: given a feature vector X and a (typically unobserved) qualitative response Y taking
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Mariia Okuneva, M.Sc. Data Mining Home Assignment 2 In this home assignment you will again use Smarket data set which is part of the ISLR library. In the rst part, you will write your own function designed to t an LDA model to the train set and compare its performance with a pre-impelemnted lda() function.
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Data mining Prof. Dr. Matei Demetrescu Summer 2020 Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 30 Today’s outline Statistical learning 1 The data mining process 2 Supervised learning 3 Unsupervised learning 4 Up next Statistics and Econometrics (CAU Kiel) Summer 2020 2 / 30 The data mining process Outline 1 The data mining
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Data mining Institute of statistics and econometrics (University of Kiel) June 1, 2020 Contents Preliminaries 1 1 Statistical learning 3 1.1 Fromstatisticstostatisticallearning …………………. 3 1.2 Supervisedlearning………………………….. 4 1.3 Unsupervisedlearning ………………………… 5 2 Supervised learning: some background 6 2.1 Errorquantification………………………….. 6 2.2 Learningforprediction………………………… 10 2.3 Leaningwithmanyfeatures ……………………… 12 3 Linear prediction and classification 14 3.1 Predictionwithlinearregression…………………….
Data mining Prof. Dr. Matei Demetrescu Summer 2020 Statistics and Econometrics (CAU Kiel) Summer 2020 1 / 34 Today’s outline Basics of prediction and classification 1 Error quantification 2 Learning for prediction 3 Leaning with many features 4 Up next Statistics and Econometrics (CAU Kiel) Summer 2020 2 / 34 Error quantification Outline 1 Error
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Mariia Okuneva, M.Sc. Part 1 Data Mining Home Assignment 1 In the rst part of this home assignment you will use data on house sales in King County. You will write your own function designed to t a linear model and compare its performance with lm() function. Description of the variables: • price: price of
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R and RStudio Introduction to R Mariia Okuneva • R is the name of the programming language designed for statistical computing and graphics, RStudio is a convenient interface. • We write all the commands in this script window and send them to the console. To clear console use Ctrl + L. Advantages of R •
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Prof. Dr. Matei Demetrescu University of Kiel Institute for Statistics and Econometrics Summer 2020 Data Mining Course description The course provides a statistical introduction to methods designed for analyzing large and complex data sets and relations. The focus is on regression and classification methods. We start in a parametric setup with linearity, but move on
Q&A, May 27th data mining §³§ä§â.1 data mining §³§ä§â.2 data mining §³§ä§â.3 data mining §³§ä§â.4
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