As Rustin Cohle said in True Detective, “Time is a flat circle,” so welcome back to the beginning of our Analytics Journey! Previously, we cycled through the CRISP-DM process from beginning to end, explaining the stages as well as the way we approach our Data Science life cycle at Ruths.ai. We have strived to demonstrate the importance of melding the human element with quantitative rigor. Now, we will re-iterate through the steps as all good analytics processes will do, looking for ways to strengthen our model. This time through, we will move from the theoretical to practical with an eye towards enacting the stages in the real world.
We often use Excel templates to calculate the Call and Put option prices with required parameters and the relationship between the parameters are expressed in Excel Functions. One tricky problem with the Excel Function is that if you change the cell position, accidentally click on a wrong cell, or if you want to export the results and utilize the templates again, the embedded functions may change accordingly and generate wrong answers.
Bookmarks, Tags & Lists
- All three of these Spotfire features are used to capture pieces of an analysis, but have you ever wondered when to use one versus the other?
- Have you ever wondered about the subtle differences between them?
Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation stages. Next, we examine the final stage: Deployment.
The. Final. Stage. Now, we just have to turn this thing on and reap the rewards, right?
Unfortunately, Deployment does not just happen with the push of a George Jetson button.
- Are unsupported HTML tags confusing and maddening?
- Would you like to make text areas look more visually appealing and professional?
Support Vector Machines (SVMs) is one of the most popular and most widely used machine learning algorithms today. It is robust and allows us to tackle both classification and regression problems. In general, SVMs can be relatively easy to use, have good generalization performance, and often do not require much tuning. Follow this link for further information regarding support vector machines. To help illustrate the power of SVMs, we thought it would be useful to go through an example using a custom template we have created for SVMs.
The “spTimer” package uses three Bayesian models to fit Spatio-Temporal Data. The data may be given at sparse spatial stations, where observations at each station are considered time series. The package can model the residual spatio-temporal variation to measure uncertainty. It also gives flexibility to customize covariance function selection, the hyper-parameters of the prior distributions and the tuning parameters for the implemented MCMC algorithms.
To veteran Spotfire users, the distinction between Marking, Filtering, and Limiting might seem obvious; however, to an uninitiated member, some similarities might cause confusion. In fact, one often can obtain the same exact result using combinations of Marking, Filtering, and Limiting. All the methods allow the user to make a click in one area that affects other visualizations. All the methods in their own way highlight a subset of the data.