Branch of Machine Learning dealing with Supervised Learning.
Normality has been shown to help provide more stable machine learning models and improve the accuracy of these models in the long term. The problem is everyone is using their power transformer wrong. This 2-minute guide will show you how…
Natural Language Processing is a cornerstone of data science. With so many different tools out there, sometimes it’s easy to get a bit confused. One thing many data scientists (including me) mess up on is the differences between the CountVectorizer…
We’ve all been there; you’ve worked night and day to finally get an accurate model for your dataset. You’ve finally got an output from your model – but it’s scaled. What do you do? How do you reverse standardization? In…
When it comes to classification problems, your population data is critical. While investigating our target class, we often notice disproportionate sampling. In these instances, what do we do? The Stratified sampling technique means that your sample data will have the…
In data science, some underlying assumptions are made when you use specific machine learning models. Many of these assumptions are based on the distributions of your data. With this 5-minute read, we will explore how to find the current distributions…
Correlation seems simple on the surface. As one thing gets larger, something either gets larger or smaller. While at a high level, this is generally true, once we go further into correlation, there is much more it can tell us.…
As data scientists, we’re interested in solving future problems. We do this by finding patterns and trends in data, then applying these insights in real-time. Bayes theorem (the backbone of Bayes Classification) is built upon class-level prior probability, and this…
In data science, when trying to discover the trends and patterns inside of data, you may run into many different scenarios. For example, you could run into a situation where the data is not linear, you have more than one…
