Random Forest models have risen significantly in their popularity – and for some real good reasons. They can be applied quickly to any data science problems to get first set of benchmark results. They are incredibly powerful and can be implemented quickly out of the box. Please note that I am saying first set of results and not all results. Because they have a few limitations as well. In this article, I’ll introduce you to the most interesting aspects of applying Random Forest in your predictive models.
Anyone performing computationally heavy work, such as Monte Carlo methods, will know that these are usually computationally expensive algorithms which, even in modern hardware, can result in waiting times in the magnitude of hours, days and even weeks. These long running times coupled with the fact that in certain cases it is not easy to accurately predict how long a certain number of iterations will take, usually leads to a tiresome behaviour of constantly checking for good (or bad) news. Although it is perfectly possible to specify that your simulation should stop after a certain amount of time (especially valid for very long simulations), this doesn’t seem to be the standard practice. In this post I’ll detail my current setup for being notified exactly of when simulations are finished.
This is the third post in the longitudinal data series. Previously, we introduced what longitudinal data is, how we can convert between long and wide format data-sets, and a basic multilevel model for analysis. Apparently, the basic multilevel model is not quite enough to analyse our imaginary randomised controlled trial (RCT) data-set. This post is going to continue our analysis and introduce a proper way to handle treatment effects in multilevel models.
Hotel industry is another industry where effective use of analytics can change dramatically how business is run. It is another data rich industry that captures huge volumes of data of different types, including video, audio, and Web data. However, for most hoteliers data remains an underused and underappreciated asset. Hoteliers capture loyalty information, for example, but few go beyond loyalty tier in how they consistently view and take action with their guests. With analytical exploitation of their data, hoteliers can go beyond their traditional loyalty programs and deepen their knowledge of guests in order to develop a more granular understanding of segment behavior, needs, and expectations; identify profitable customer segments and their buying preferences; and identify opportunities to attract new guests. But all that starts with having clear customer-driven vision, before embarking on Integrating and standardizing guest data from multiple channels, systems and properties into a unified, accurate view of all interactions.