Getting to Know Big Data: In the Language of Beginners

Disclaimer: I have just completed a Specialist Diploma in Data Science with Specialization in Big Data & Streaming Analytics, but I am by no means an expert in this area. What I hope to achieve from this post is to simplify what I have learnt from the programme, to explain to laypeople who wish to … Continue reading Getting to Know Big Data: In the Language of Beginners

Original Research: Is Using Fast-and-Frugal Trees Better Than Machine-Learning Trees?

In this series on Original Research, I will be sharing about my findings from some of the mini-projects that I have carried out on my own. Fast-and-frugal trees (FFTs) are a specific type of classification decision tree with sequentially ordered cues, where every cue has two branches and one branch is an exit point (Martignon … Continue reading Original Research: Is Using Fast-and-Frugal Trees Better Than Machine-Learning Trees?

Machine Learning vs Human Learning Part 2: Why ML is Unlike Human Learning

In the previous post, I briefly explained the three main types of machine learning, and compared them to their human learning theory equivalents. Using Dolan & Dayan’s (2013) discussion on goals and habits in the brain, this post will discuss about the difference between model-based learning and model-free learning, before showing how it helps to … Continue reading Machine Learning vs Human Learning Part 2: Why ML is Unlike Human Learning

Machine Learning vs Human Learning Part 1: Types of ML and Their Human Learning Theory Equivalents

The origins of machine learning are not easy to determine as it is a field that borrowed many ideas from various disciplines to evolve into what it is today. Some consider machine learning to have developed from statistics as most of its methods are statistically based, while others believe that one of the first few … Continue reading Machine Learning vs Human Learning Part 1: Types of ML and Their Human Learning Theory Equivalents

Fuzzy Buzzy: Sussing Out the “Fuzzy Logic” of Buzzwords in Data Science

Disclaimer: This post does not involve the actual Fuzzy Logic. The term was originally intended as just a pun, but I later realised that it also demonstrates how the improper use of buzzwords can be quite misleading. Apologies for any confusion caused. With the growing popularity of Data Science, many buzzwords have been loosely thrown … Continue reading Fuzzy Buzzy: Sussing Out the “Fuzzy Logic” of Buzzwords in Data Science

Confused by The Confusion Matrix Part 2: ‘Accuracy’ is But One of Many Measures of Accuracy…

In the previous post, I explained the concept of interpreting a confusion matrix by clarifying all the different terms that actually mean the same thing. Unfortunately, the confusion does not end there. If you finished reading the previous post, you should have probably come to the realisation that a simple Hit Rate or False Alarm … Continue reading Confused by The Confusion Matrix Part 2: ‘Accuracy’ is But One of Many Measures of Accuracy…

Confused by The Confusion Matrix: What’s the difference between Hit Rate, True Positive Rate, Sensitivity, Recall and Statistical Power?

If you tried to answer the question in the title, you'll be disappointed to find out that it is actually a trick question - there is essentially no difference in the listed terms. Just like the issue mentioned in ANCOVA and Moderation, different terms are often used for the same thing, especially when they belong … Continue reading Confused by The Confusion Matrix: What’s the difference between Hit Rate, True Positive Rate, Sensitivity, Recall and Statistical Power?