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

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). A set of mutually constraining choices determines what counts as an…
Original Post: 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!

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 own specimen from your garden, and you carefully compare it…
Original Post: 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” 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 zero. At what temperature do you prefer your first cup…
Original Post: The Kernel Trick in Support Vector Machines: Seeing Similarity in More Intricate Dimensions

The Mad Hatter Explains Support Vector Machines

“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 she looked down to see a compass painted on the floor below her. Arrows pointed in every direction with…
Original Post: The Mad Hatter Explains Support Vector Machines

When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

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 including price in the feature list, and the final worth of the product is a function of its feature…
Original Post: When Choice Modeling Paradigms Collide: Features Presented versus Features Perceived

Choice Modeling with Features Defined by Consumers and Not Researchers

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 whether the input was of type “buy” or not. As…
Original Post: 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

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 for code from different communities, each with their own set…
Original Post: 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?"

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 need to define our terms. We begin by making a list…
Original post: A Data Science Solution to the Question "What is Data Science?"
Source: R-bloggers