If you haven’t already had a read of the other parts in this series, check them out: Model Interpretability Part 1: The Importance and Approaches Model Interpretability Part 2: Global Model Agnostic Methods To recap from Part 1: Local Interpretability aims to capture individual predictions. It focuses on the specific…

As mentioned in Part 1 of Model Interpretability, the flexibility of model-agnostics is the greatest advantage, being the reason why they are so popular. Data Scientists and Machine Learning Engineers can use any machine learning model they wish as the interpretation method can be applied to it. …

Amazingly, we can use Machine Learning to make wonderful predictions and help us greatly in the decision-making process. However, they lack interpretability: The ability for humans to easily understand their process and how it derived particular outcomes. ML model calculations can be so complex that some may find it near…

In 2009, Google Chief Economist Hal Varian told McKinsey Quarterly that “the sexy job in the next ten years will be statisticians.”. Here we are in 2022, where it seems like Data Science is the new buzzword around the recruitment industry. If you look at recruitment sites, you will see…

Over the years, Deep Learning has really taken off. This is because we have access to a lot more data and more computational power, allowing us to produce more accurate outputs and build models more efficiently. The rise in Deep Learning has been applied in different sectors such as speech…