As its names suggest, “outliers” refers to a group or singular piece of information that doesn’t seem to fit in with the other information. Our job as data scientists is to identify and deal with these potential outliers. In this…
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…
As data scientists, you’ll often face datasets too large to understand. With the recent advances in machine learning, we can visualize nearly any variable, parse massive amounts of text into understandable pieces and find hidden trends within our datasets. After…
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.…
While K means clustering is one of the most famous clustering algorithms, what happens when you are clustering categorical variables or dealing with binary data? K means clustering depends on the data points being continuous, where an average (mean) is…
Seeing what features are most important in your models is key to optimizing and increasing model accuracy. Feature importance is one of the most crucial aspects of machine learning, and sometimes how you got to an answer is more important…
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…
Evaluating a clustering algorithm is much different than evaluating a classification or regression machine learning algorithm. In a classification problem, labels will be given for the data points, giving you a reference point to create your accuracy measure. What happens…