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Efficiently Exploring Multilevel Data with Recursive Partitioning.
Efficiently Exploring Multilevel Data with Recursive Partitioning.

... hypotheses are exhausted, it can be difficult to determine how best to explore the dataset to discover hidden relationships that could help to inform future research. Naturally, this practice is often done “by hand.” That is, the researcher in question will run multiple tests with different combinat ...
Classification in spatial data mining
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... – typically used in remote sensing image classification problems • see Appendix in 7.8 – In conditional probability we calculate the probability of A when we know that B has happened – In Bayes the question is: knowing that B happened what is the ...
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... Given data about some cars: engine size, number of seats, petrol / diesel, has airbag or not, price  Problem 1: Given engine size of a new car, what is likely to be the price?  Problem 2: Given the engine size of a new car, is it likely that the car is run by petrol?  Problem 3: Given the engine ...
Springer Series in Statistics
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... Tibshirani, and Jerome Friedman (Springer-Verlag, 2001). I saw in the book a heroic effort to integrate a very wide variety of data analysis tools. I learned from the book and was then able to approach more primary material within a useful framework. This book is my attempt to integrate some of the s ...
Using SAS/Insight as an Introductory Data Mining Platform
Using SAS/Insight as an Introductory Data Mining Platform

... single-season batting statistics. The adjusted rsquared statistic (0.35) suggests an improvement over the single-season fielding model. Batting average, RB Is, and bases on balls all were statistically significant and had the correct sign, but runs and home runs were not significant. Multi-collinear ...
Chapter 6 - Faculty & Research
Chapter 6 - Faculty & Research

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Download Syllabus
Download Syllabus

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Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing

... between the independent variables and the log of the odds of the dependent variable, transformations can be used to make the independent variables more linear. Examples of transformations include the square, cube, square root, cube root, and the log. Some complex methods have been developed to deter ...
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Data Mining: An Overview of Methods and Techniques
Data Mining: An Overview of Methods and Techniques

... how the risk index is lower for the most responsive customers. This is common in direct response and demonstrates ‘adverse selection’. In other words, the riskier prospects are often the most responsive. At some point in the process, a decision is made to mail a percent of the file. In this case, yo ...
REMARKS FOR PREPARING TO THE EXAM (FIRST ATTEMPT
REMARKS FOR PREPARING TO THE EXAM (FIRST ATTEMPT

... This note should give you some useful hints on topics which are the basis for preparing the exam on the Data Mining and Data Analysis course for Software Engineering specialization at Computer Science. However, you should treat them as a kind of warnings what seems to be the most important in Magdal ...
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing

... between the independent variables and the log of the odds of the dependent variable, transformations can be used to make the independent variables more linear. Examples of transformations include the square, cube, square root, cube root, and the log. Some complex methods have been developed to deter ...
Data Mining: An Overview
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Spatial OLAP for ArcGIS Desktop

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Find the Best Prospects for a New Product by Using a Data Mining Model
Find the Best Prospects for a New Product by Using a Data Mining Model

... This paper will introduce how to build up a data mining model using SAS Enterprise Miner, how to assess model performance, and how to validate a model by targeting the 1000 best customers for a new product. ...
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Multinomial logistic regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit, maximum entropy (MaxEnt) classifier, conditional maximum entropy model.
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