Let’s see how much research has been done in term of COVID-19 in Malaysia. In this analysis, we are going to use Scopus database to access the relevant research or papers.
This post will not go very detail in each of the approach of hyperparameter tuning. This post mainly aims to summarize a few things that I studied for the last couple of days.
These are some of the packages that I find useful for data exploration. Basically, this post serves more as my note for future reference. I will list out packages (and some awesome functions from that particular package) rather than specific functions.
I just watched a youtube video by Andrew Couch about his commonly used function in readr, stringr, and forcats packages. Although, I have used forcats package before, I realised that I have not fully utilised all of its function.
Overview Imbalance data happens when there is unequal distribution of data within a categorical outcome variable. Imbalance data occurs due to several reasons such as biased sampling method and measurement errors.
I have been reading about lost functions and optimisers in deep learning for the last couple of days when I stumble upon the term Exponentially Weighted Average (EWA). So, in this post I aims to explain my understanding of EWA.
First of all, this write up is mean for a beginner in R.
Things can be done in many ways in R. In facts, R has been very flexible in this regard compared to other statistical softwares.
I have heard quite a several times that apply function is faster than loop function in R. Loop function is said to be inefficient, though in certain situation loop is the only way.