Joel Cadwell | R-bloggers

Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

June 5, 2016 | Joel Cadwell

Someone decided what data ought to go into the matrix. They placed the objects of interest in the rows and the features that differentiate among those objects into the columns. Decisions were made either to collect information or to store what was gathered for other purposes (e.g., data mining). ...

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Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

June 5, 2016 | Joel Cadwell

Someone decided what data ought to go into the matrix. They placed the objects of interest in the rows and the features that differentiate among those objects into the columns. Decisions were made either to collect information or to store what was gathered for other purposes (e.g., data mining)....

[Read more...]

Using Support Vector Machines as Flower Finders: Name that Iris!

May 25, 2016 | Joel Cadwell

Nature field guides are filled with pictures of plants and animals that teach us what to look for and how to name what we see. For example, a flower finder might display pictures of different iris species, such as the illustrations in the plot below. You have in hand your ...

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Using Support Vector Machines as Flower Finders: Name that Iris!

May 25, 2016 | Joel Cadwell

Nature field guides are filled with pictures of plants and animals that teach us what to look for and how to name what we see. For example, a flower finder might display pictures of different iris species, such as the illustrations in the plot below. Y...

[Read more...]

The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

May 23, 2016 | Joel Cadwell

The "kernel" is the seed or the essence at the heart or the core, and the kernel function measures distance from that center. In the following example from Wikipedia, the kernel is at the origin and the different curves illustrate alternative depictions of what happens as we move away from ...

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The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

May 23, 2016 | Joel Cadwell

The "kernel" is the seed or the essence at the heart or the core, and the kernel function measures distance from that center. In the following example from Wikipedia, the kernel is at the origin and the different curves illustrate alternative depiction...

[Read more...]

The Mad Hatter Explains Support Vector Machines

May 9, 2016 | Joel Cadwell

"Hatter?" asked Alice, "Why are support vector machines so hard to understand?" Suddenly, before you can ask yourself why Alice is studying machine learning in the middle of the 19th century, the Hatter disappeared. "Where did he go?" thought Alice as ...

[Read more...]

The Mad Hatter Explains Support Vector Machines

May 9, 2016 | Joel Cadwell

"Hatter?" asked Alice, "Why are support vector machines so hard to understand?" Suddenly, before you can ask yourself why Alice is studying machine learning in the middle of the 19th century, the Hatter disappeared. "Where did he go?" thought Alice as ...

[Read more...]

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

April 3, 2016 | Joel Cadwell

What is the value of a product feature? Within a market-based paradigm, the answer is the difference between revenues with and without the feature. A product can be decomposed into its features, each feature can be assigned a monetary value by includin...

[Read more...]

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

April 3, 2016 | Joel Cadwell

What is the value of a product feature? Within a market-based paradigm, the answer is the difference between revenues with and without the feature. A product can be decomposed into its features, each feature can be assigned a monetary value by includin...

[Read more...]

Choice Modeling with Features Defined by Consumers and Not Researchers

March 25, 2016 | Joel Cadwell

Choice modeling begins with a researcher "deciding on what attributes or levels fully describe the good or service." This is consistent with the early neural networks in which features were precoded outside of the learning model. That is, choice modeli... [Read more...]

Choice Modeling with Features Defined by Consumers and Not Researchers

March 25, 2016 | Joel Cadwell

Choice modeling begins with a researcher "deciding on what attributes or levels fully describe the good or service." This is consistent with the early neural networks in which features were precoded outside of the learning model. That is, choice modeling can be seen as learning the feature weights that recognize ... [Read more...]

Understanding Statistical Models Through the Datasets They Seek to Explain: Choice Modeling vs. Neural Networks

March 21, 2016 | Joel Cadwell

R may be the lingua franca, yet many of the packages within the R library seem to be written in different languages. We can follow the R code because we know how to program but still feel that we have missed something in the translation.R provides an o... [Read more...]

Understanding Statistical Models Through the Datasets They Seek to Explain: Choice Modeling vs. Neural Networks

March 21, 2016 | Joel Cadwell

R may be the lingua franca, yet many of the packages within the R library seem to be written in different languages. We can follow the R code because we know how to program but still feel that we have missed something in the translation.R provides an open environment ... [Read more...]

A Data Science Solution to the Question "What is Data Science?"

January 8, 2016 | Joel Cadwell

As this flowchart from Wikipedia illustrates, data science is about collecting, cleaning, analyzing and reporting data. But is it data science or just or a "sexed up term" for Statistics (see embedded quote by Nate Silver)? It's difficult to separ... [Read more...]

A Data Science Solution to the Question "What is Data Science?"

January 8, 2016 | Joel Cadwell

As this flowchart from Wikipedia illustrates, data science is about collecting, cleaning, analyzing and reporting data. But is it data science or just or a "sexed up term" for Statistics (see embedded quote by Nate Silver)? It's difficult to separate the two at this level of generality, so perhaps we ... [Read more...]

Modeling How Consumers Simplify the Purchase Process by Copying Others

December 23, 2015 | Joel Cadwell

A Flower That Fits the Bill Marketing borrows the biological notion of coevolution to explain the progressive "fit" between products and consumers. While evolutionary time may seem a bit slow for product innovation and adoption, the same metaphor can be found in models of assimilation and accommodation from cultural and ... [Read more...]

Modeling How Consumers Simplify the Purchase Process by Copying Others

December 23, 2015 | Joel Cadwell

A Flower That Fits the BillMarketing borrows the biological notion of coevolution to explain the progressive "fit" between products and consumers. While evolutionary time may seem a bit slow for product innovation and adoption, the same metaphor can be... [Read more...]

BayesiaLab-Like Network Graphs for Free with R

December 18, 2015 | Joel Cadwell

My screen has been filled with ads from BayesiaLab since I downloaded their free book. Just as I began to have regrets, I received an email invitation to try out their demo datasets. I was especially interested in their perfume ratings data. In this monadic product test, each of 1,321 French ... [Read more...]

BayesiaLab-Like Network Graphs for Free with R

December 18, 2015 | Joel Cadwell

My screen has been filled with ads from BayesiaLab since I downloaded their free book. Just as I began to have regrets, I received an email invitation to try out their demo datasets. I was especially interested in their perfume ratings data. In this mo... [Read more...]

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Articles by Joel Cadwell

Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

Using Support Vector Machines as Flower Finders: Name that Iris!

Using Support Vector Machines as Flower Finders: Name that Iris!

The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

The Mad Hatter Explains Support Vector Machines

The Mad Hatter Explains Support Vector Machines

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

Choice Modeling with Features Defined by Consumers and Not Researchers

Choice Modeling with Features Defined by Consumers and Not Researchers

Understanding Statistical Models Through the Datasets They Seek to Explain: Choice Modeling vs. Neural Networks

Understanding Statistical Models Through the Datasets They Seek to Explain: Choice Modeling vs. Neural Networks

A Data Science Solution to the Question "What is Data Science?"

A Data Science Solution to the Question "What is Data Science?"

Modeling How Consumers Simplify the Purchase Process by Copying Others

Modeling How Consumers Simplify the Purchase Process by Copying Others

BayesiaLab-Like Network Graphs for Free with R

BayesiaLab-Like Network Graphs for Free with R

Articles by Joel Cadwell

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