One Page R: A Survival Guide to Data Science with R. A collection of useful one-page resources for a data miner, data scientist, and/or a decision scientist.
The modules include code, lectures, and one-page recipes for getting things done. Graham Williams, the founder of togaware, the developer of Rattle, free and open-source data mining software based on R, and author of Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R) book, has summarized useful R resources in One Page R: A Survival Guide to Data Science with R The include tools for the data miner, or the data scientist, and or the decision scientist.
3 Ways to Test the Accuracy of Your Predictive Models. 3 different methods for testing accuracy of predictive models from 3 leading analytics experts - Karl Rexer, John Elder, and Dean Abbott explain using lift charts, randomization testing, and bootstrap sampling.
Plotting Success, Victoria Garment, Jan 29, 2014 In data mining, data scientists use algorithms to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. These patterns are used to create predictive models that try to forecast future behavior. .. There are many different tests to determine if the predictive models you create are accurate, meaningful representations that will prove valuable to your organization-but which are best? Compare Predictive Model Performance Against Random Results With Lift Charts and Decile Tables Rexer's firm creates models that help clients determine how likely people are to complete a binary behavior.
In this chart, the predictive model is represented by the curved blue line. Code Snippets. Data Mining and Analytic Technologies (Kurt Thearling) Www.sgim.org/userfiles/file/AMHandouts/AM05/handouts/pa08.pdf. GGobi data visualization system. Www2.sas.com/proceedings/forum2008/166-2008.pdf. Comparison of data analysis packages: R, Matlab, SciPy, Excel, SAS, SPSS, Stata.
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Thanks for visiting! I posted an earlier version of this data mining blog list in a previously on DMR. Here is an updated version (blogs recently added to the list have the logo “new”). I will keep this version up-to-date. You can access it at any time from the DMR top bar. Ryan Rosario. Web Analytics. Training. WikiTopics. Statistics. Machine Learning. Software. Techniques. Part II. Heuristic Andrew. Data Mining Community's Top Resource. What Is Data Mining? This chapter provides a high-level orientation to data mining technology.
What Is Data Mining? Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). The key properties of data mining are: Automatic discovery of patternsPrediction of likely outcomesCreation of actionable informationFocus on large data sets and databases Data mining can answer questions that cannot be addressed through simple query and reporting techniques.
Automatic Discovery Data mining is accomplished by building models. Data mining models can be used to mine the data on which they are built, but most types of models are generalizable to new data. Prediction Many forms of data mining are predictive. Grouping Actionable Information. Kaggle.