1774 search results for "regression"

The hat trick

July 3, 2013
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The hat trick

In his book Quantum Computing Since Democritus, Scott Aaronson poses the following question: Suppose that you’re at a party where every guest is given a hat as they walk in. Each hat has either a pineapple or a watermelon on top, picked at random with equal probability. The guests don’t get to see the fruit

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In case you missed it: June 2013 Roundup

July 3, 2013
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In case you missed them, here are some articles from June of particular interest to R users: You can create a Word document from a template and an R script with the R2DOCX package. Joe Rickert reviews books and other resources for learning about time series analysis in R. Timely Portfolio covers 15 years of history of time series...

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Predictive analysis on Web Analytics tool data

July 3, 2013
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Predictive analysis on Web Analytics tool data

In our previous webinar, we discussed on predictive analytics and basic things to perform predictive analysis. We also discussed on an eCommerce problem and how it can be solved using predictive analysis. In this post, I will explain R script that I used to perform predictive analysis during webinar. Before I explain about R script,

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Learning Time Series with R

June 27, 2013
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by Joseph Rickert Late last Saturday afternoon I was reading in my usual spot at the Dana Street Coffee House in Mt. View. A stranger walking by my table noticed my copy of Madsen’s Time Series Analysis (sitting there untouched again) said he needed to learn something about time series and asked if I could recommend a book. He...

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Fun with Fremont Bridge Bicyclists

June 27, 2013
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Fun with Fremont Bridge Bicyclists

Given the title of this post and its proximity to the Solstice, you will be disappointed to know that I am not writing about naked bicyclists. I apologize for any false hope I may have instilled in you.On October 11th, 2012, the city of Seattle, WA beg...

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Natural language processing tutorial

June 25, 2013
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Natural language processing tutorial

Introduction This will serve as an introduction to natural language processing. I adapted it from slides for a recent talk at Boston Python. We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. The goal is to provide a reasonable baseline on top of which more complex natural language processing can be...

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Natural Language Processing Tutorial

June 25, 2013
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Natural Language Processing Tutorial

Introduction This will serve as an introduction to natural language processing. I adapted it from slides for a recent talk at Boston Python. We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. The goal is to provide a reasonable baseline on top of which more complex natural language processing can be done, and...

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Natural Language Processing Tutorial

June 25, 2013
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Natural Language Processing Tutorial

Introduction This will serve as an introduction to natural language processing. I adapted it from slides for a recent talk at Boston Python. We will go from tokenization to feature extraction to creating a model using a machine learning algorithm. The goal is to provide a reasonable baseline on top of which more complex natural language processing can be done, and...

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GRNN and PNN

June 23, 2013
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GRNN and PNN

From the technical prospective, people usually would choose GRNN (general regression neural network) to do the function approximation for the continuous response variable and use PNN (probabilistic neural network) for pattern recognition / classification problems with categorical outcomes. However, from the practical standpoint, it is often not necessary to draw a fine line between GRNN

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Measuring Associations

June 20, 2013
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Measuring Associations

In Chapter 18, we discuss a relatively new method for measuring predictor importance called the maximal information coefficient (MIC). The original paper is by Reshef at al (2011). A summary of the initial reactions to the MIC are Speed and Tibshirani (and others can be found here). My (minor) beef with it is the lack...

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