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Data Science at INCAE
Приєднався 29 чер 2017
RapidMiner Tutorial - How to predict for new data and save predictions to Excel
Once we've decided on the most suitable model for a particular prediction problem, how do we predict for new data? How do we save our new predictions to Excel to share them?
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Відео
RapidMiner Tutorial - How to run a linear regression using cross-validation in RapidMiner
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How do we run a simple regression in RapidMiner? How do we protect ourselves from over-fitting our model using various training as well as test-sets through cross-validation?
RapidMiner Tutorial - How to create association rules for cross-selling or up-selling
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How do we create association rules given some transactional data? How do we interpret the created rules and use them for cross- or up-selling?
RapidMiner Tutorial - How to perform a simple cluster analysis using k-means
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How can we perform a simple cluster analysis in RapidMiner? How can we interpret clusters and decide on how many to use?
How to perform simple one and two way Sensitivity Analyses using Data Tables in Excel
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A short tutorial how to perform simple one (checking the effects of one variable, e.g. price on another variable of interest, e.g. profit) and two way (checking the effect of two variables at the same time, e.g. price and cost, on another variable of interest, e.g. profit) Sensitivity Analyses using Data Tables in Excel
Radiant Tutorial: What is R2? Calculating, and interpreting R2
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A tutorial on how to calculate and interpret R2 in Radiant.
Radiant Tutorial: Simple Time Series Analysis
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How do we perform a simple time series analysis in Radiant? How do we predict for in sample observations, and how to we forecast for out-of-sample data ahead of time?
Radiant Tutorial: Logistic Regression in Radiant
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Questions covered: How do we run a simple logistic regression in Radiant? How do we predict new observations, work with different thresholds for classification and create a confusion table to see how well our model classifies?
Radiant Tutorial: Construct and work with dummy variables
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This tutorial deals with how to use, construct and work with dummy variables in Radiant
Radiant Tutorial: Forecasting new observations
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A tutorial on how to predict/forecast individual as well as large numbers of new observations in Radiant (radiant-rstats.github.io/docs/index.html)
Radiant Tutorial: Simple Linear Regression
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Tutorial on how to run a simple linear regression in Radiant (radiant-rstats.github.io/docs/index.html)
This example is obviously for numerical data - what about textural data? Does one normalize textural data for k-means clustering as well? If so, it it the same process? Thanks in advance for your reply!
I get a non-numerical attribute error when trying to run the work flow with the performance block. I've tried the remove useless attribute module but no joy. is there a work around or is it easiest to reimport and drop the species name?