The internet is evolving day by day, and when users shop online, they are flooded with thousands of results, leaving them in a dilemma to choose the best possible product that suits their requirements. Have you ever thought of how Google Ads precisely knew what you need and display…

Everything you need to know about a top algorithm in Machine Learning

Each year, Risk Quant Europe Conference, a conference well-attended by practitioners from banking, asset management, insurers as well as academics from Europe, selects two papers to present in their annual conference.

For 2018, our paper is lucky to be one of the two winning papers selected by the Advisory Board for the conference to be held in London. Please feel free to check out our paper titled CDS Rate Construction Methods by Machine Learning…

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in…

Cross Validation is often used as a tool for model selection across classifiers. As discussed in detail in the following paper https://ssrn.com/abstract=2967184, Cross Validation is typically performed in the following steps:

• Step 1: Divide the original sample into K sub samples; each subsample typically has equal sample size and is referred to as one fold, altogether,…

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

• Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
• Support Vector Machine: e.g., the optimal choice of Kernel Functions
• Ensemble: e.g., the number of Learning Cycles for Bagging.
• Discriminant Analysis: e.g., Linear/Quadratic; regularization…

Past literature show that the comparisons of classifier's performance are specific to the types of datasets (e.g., Pharmaceutical industry data) used; i.e., some classifiers may perform better in some context than others. A paper titled CDS Rate Construction Methods by Machine Learning Techniques conducts the performance comparison exclusively in the context of financial market by applying a wide range of classifiers to provide solution to so-called Shortage of…

One important goal of data science is to help decision makers make better decisions. Markov…

We have tried to synthesize the most disruptive big data use cases into a compact . 3 Minute video

at https://www.youtube.com/watch?v=UWPUxLr-u9k

It covers 6 use cases , 4 healthcare data streams and hopefully sets the stage for curating more use cases in an area which truly needs a lot of healthy transformations !!!

There is no question that the USA (in fact, most of the world) would be well-served with more quantitatively capable people to work in business and government. However, the current hysteria over the shortage of data scientists is overblown. To illustrate why, I am going to use an example from air travel.

On a recent trip from Santa Fe, NM to Phoenix, AZ, I tracked the various times: