The main idea of support vector machine is to find the optimal hyperplane (line in 2D, plane in 3D and hyperplane in more than 3 dimensions) which maximizes the margin between two classes. In this case, two classes are red and blue balls. In layman’s term, it is finding the optimal separating boundary to separate two classes (events and non-events).
Deepanshu then goes on to implement this in R.
Let’s start thinking in a logical way the steps that one should perform once we have the data imported into R.
- The first step would be to discover what’s in the data file that was exported. To do this, we can:
- Use head function to view few rows from the data set. By default, head shows first 5 rows. Ex:
- str to view the structure of the imported data. Ex:
- summary to view the data summary. Ex:
There’s a lot to data analysis, but this is a good start.
Clarify who will be making the decision – man, or machine? Humans have powers of discretion that machines sometimes lack, but are much slower than a silicon-based system, and only able to make decisions one-at-a-time, one-after-another. If we chose to put a human in the loop, we are normally in “please-update-my-dashboard-faster-and-more-often” territory.
It is important to be clear about decision-latency. Think about how soon after a business event you need to take a decision and then implement it. You also need to understand whether decision-latency and data-latency are the same. Sometimes a good decision can be made now on the basis of older data. But sometimes you need the latest, greatest and most up-to-date information to make the right choices.
There are some good insights here.
When reading about Bayesian statistics, you regularly come across terms like “objective priors“, “prior odds”, “prior distribution”, and “normal prior”. However, it may not be intuitively clear that the meaning of “prior” differs in these terms. In fact, there are two meanings of “prior” in the context of Bayesian statistics: (a) prior plausibilities of models, and (b) the quantification of uncertainty about model parameters. As this often leads to confusion for novices in Bayesian statistics, we want to explain these two meanings of priors in the next two blog posts*. The current blog post covers the the first meaning of priors.
Priors are a big differentiator between the Bayesian statistical model and the classical/frequentist statistical model.
Within a given project, we know that at the beginning of our first ever project we may not have a lot of domain knowledge, or there might be problems with the data or the model might not be valuable enough to put into production. These things happen, and the really nice thing about the CRISP-DM model is it allows for us to do that. It’s not a single linear path from project kick-off to deployment. It helps you remember not to beat yourself up over having to go back a step. It also equips you with something upfront to explain to managers that sometimes you will need to bounce between some phases, and that’s ok.
This is another place in which “iterate, iterate, iterate” ends up being the best answer available.
An open source approach helps build a foundation for other models attempting to forecast violations at food establishments. The analytic code is written in R, an open source, widely-known programming language for statisticians. There is no need for expensive software licenses to view and run this code.
Read on for more details and check out their GitHub repo.
Now, suppose you were able to find a good function to model your data. With that, we are able to predict future values for our small dataset.
One important thing about the predict() function in R is that it expects a similar dataframe with the same column name and type as the one you used in your model.
Click through for several examples.
The heart of his critique is this: data science is changing very fast, and any tool that you learn will eventually become obsolete.
This is absolutely true.
Every tool has a shelf life.
Every. single. one.
Moreover, it’s possible that tools are going to become obsolete more rapidly than in the past, because the world has just entered a period of rapid technological change. We can’t be certain, but if we’re in a period of rapid technological change, it seems plausible that toolset-changes will become more frequent.
The thing I would tie it to is George Stigler’s paper on information theory. There’s a cost of knowing—which the commenter notes—but there’s also a cost to search, given the assumption that you know where to look. Being effective in any role, be it data scientist or anything else, involves understanding the marginal benefit of pieces of information. This blog post gives you a concrete example of that in the realm of data science.
Our group is distributing a detailed write up of the theory and operation behind our R realization of a set of sound data preparation and cleaning procedures called vtreat here: arXiv:1611.09477 [stat.AP]. This is where you can find out what
vtreatdoes, decide if it is appropriate for your problem, or even find a specification allowing the use of the techniques in non-
Renvironments (such as
Spark, and many others).
We have submitted this article for formal publication, so it is our intent you can cite this article (as it stands) in scientific work as a pre-print, and later cite it from a formally refereed source.
Or alternately, below is the tl;dr (“too long; didn’t read”) form.
The Linux Data Science Virtual Machine includes all of the tools a modern data scientist needs, in one easy-to-launch package. With it, you can try exploring data with Apache Drill, train deep neural networks for computer vision with MXNet, develop AI applications with the Cognitive Toolkit, or create statistical models with big data in R with Microsoft R Server 9.0.
They also offer a free trial, so check it out.